Survey of feature selection and extraction techniques for stock market prediction.

In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications. We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011-2022. We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles. We also describe the combination of feature analysis techniques and ML methods and evaluate their performance. Moreover, we present other survey articles, stock market input and output data, and analyses based on various factors. We find that correlation criteria, random forest, principal component analysis, and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

Pre-insemination prediction of dystocia in dairy cattle.

Dystocia or difficult calving in cattle is detrimental to the health of the afflicted cows and has a negative economic impact on the dairy industry. The goal of this study was to create a data-driven tool for predicting the calving difficulty of non-heifer cows using input variables that are known prior to the moment of insemination. Compared to past studies, we excluded input variables that can only be known during or after insemination, such as birth weight and gestation length. This makes the model suitable for informing mating decisions that could reduce the incidence of difficult calvings or mitigate their consequences. We used a dataset consisting of 131,527 calving records of Holstein cattle, from which we derived a total of 274 phenotypic features and estimated breeding values. The distribution of classes in the dataset was 96.7 % normal calvings, and 3.3 % difficult calvings. We used a gradient boosted trees (XGBoost) as the learning model and a bagging ensemble approach to deal with the extreme class imbalance. The model achieved an average area under the ROC curve of 0.73 on unseen test data. Using feature importance analysis, we identified a number of features that have a high discriminatory value for calving difficulty, including maternal and paternal breeding values, and past phenotypic measurements of the cow.

LTC-Mapping, Enhancing Long-Term Consistency of Object-Oriented Semantic Maps in Robotics.

This paper proposes LTC-Mapping, a method for building object-oriented semantic maps that remain consistent in the long-term operation of mobile robots. Among the different challenges that compromise this aim, LTC-Mapping focuses on two of the more relevant ones: preventing duplicate instances of objects (instance duplication) and handling dynamic scenes. The former refers to creating multiple instances of the same physical object in the map, usually as a consequence of partial views or occlusions. The latter deals with the typical assumption made by object-oriented mapping methods that the world is static, resulting in outdated representations when the objects change their positions. To face these issues, we model the detected objects with 3D bounding boxes, and analyze the visibility of their vertices to detect occlusions and partial views. Besides this geometric modeling, the boxes are augmented with semantic information regarding the categories of the objects they represent. Both the geometric entities (bounding boxes) and their semantic content are propagated over time through data association and a fusion technique. In addition, in order to keep the map curated, the non-detection of objects in the areas where they should appear is also considered, proposing a mechanism that removes them from the map once there is evidence that they have been moved (i.e., multiple non-detections occur). To validate our proposal, a number of experiments have been carried out using the Robot@VirtualHome ecosystem, comparing its performance with a state-of-the-art alternative. The results report a superior performance of LTC-Mapping when modeling both geometric and semantic information of objects, and also support its online execution.

Use of Convolutional Neural Networks for the Detection of u-Serrated Patterns in Direct Immunofluorescence Images to Facilitate the Diagnosis of Epidermolysis Bullosa Acquisita.

The u-serrated immunodeposition pattern in direct immunofluorescence (DIF) microscopy is a recognizable feature and confirmative for the diagnosis of epidermolysis bullosa acquisita (EBA). Due to unfamiliarity with serrated patterns, serration pattern recognition is still of limited use in routine DIF microscopy. The objective of this study was to investigate the feasibility of using convolutional neural networks (CNNs) for the recognition of u-serrated patterns that can assist in the diagnosis of EBA. The nine most commonly used CNNs were trained and validated by using 220,800 manually delineated DIF image patches from 106 images of 46 different patients. The data set was split into 10 subsets: nine training subsets from 42 patients to train CNNs and the last subset from the remaining four patients for a validation data set of diagnostic accuracy. This process was repeated 10 times with a different subset used for validation. The best-performing CNN achieved a specificity of 89.3% and a corresponding sensitivity of 89.3% in the classification of u-serrated DIF image patches, an expert level of diagnostic accuracy. Experiments and results show the effectiveness of CNN approaches for u-serrated pattern recognition with a high accuracy. The proposed approach can assist clinicians and pathologists in recognition of u-serrated patterns in DIF images and facilitate the diagnosis of EBA.

Hierarchical Approach to Classify Food Scenes in Egocentric Photo-Streams.

Recent studies have shown that the environment where people eat can affect their nutritional behavior [1]. In this paper, we provide automatic tools for personalized analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33 000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56% and 65%, respectively, clearly outperforming the baseline methods.

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures.

Delineation of curvilinear structures in images is an important basic step of several image processing applications, such as segmentation of roads or rivers in aerial images, vessels or staining membranes in medical images, and cracks in pavements and roads, among others. Existing methods suffer from insufficient robustness to noise. In this paper, we propose a novel operator for the detection of curvilinear structures in images, which we demonstrate to be robust to various types of noise and effective in several applications. We call it RUSTICO, which stands for RobUST Inhibition-augmented Curvilinear Operator. It is inspired by the push-pull inhibition in visual cortex and takes as input the responses of two trainable B-COSFIRE filters of opposite polarity. The output of RUSTICO consists of a magnitude map and an orientation map. We carried out experiments on a data set of synthetic stimuli with noise drawn from different distributions, as well as on several benchmark data sets of retinal fundus images, crack pavements, and aerial images and a new data set of rose bushes used for automatic gardening. We evaluated the performance of RUSTICO by a metric that considers the structural properties of line networks (connectivity, area, and length) and demonstrated that RUSTICO outperforms many existing methods with high statistical significance. RUSTICO exhibits high robustness to noise and texture.

Detection of u-serrated patterns in direct immunofluorescence images of autoimmune bullous diseases by inhibition-augmented COSFIRE filters.

Direct immunofluorescence (DIF) microscopy of a skin biopsy is used by physicians and pathologists to diagnose autoimmune bullous dermatoses (AIBD). This technique is the reference standard for diagnosis of AIBD, which is used worldwide in medical laboratories. For diagnosis of subepidermal AIBD (sAIBD), two different types of serrated pattern of immunodepositions can be recognized from DIF images, namely n- and u-serrated patterns. The n-serrated pattern is typically found in the most common sAIBD bullous pemphigoid. Presence of the u-serrated pattern indicates the sAIBD subtype epidermolysis bullosa acquisita (EBA), which has a different prognosis and requires a different treatment. The manual identification of these serrated patterns is learnable but challenging. We propose an automatic technique that is able to localize u-serrated patterns for automated computer-assisted diagnosis of EBA. The distinctive feature of u-serrated patterns as compared to n-serrated patterns is the presence of ridge-endings. We introduce a novel ridge-ending detector which uses inhibition-augmented trainable COSFIRE filters. Then, we apply a hierarchical clustering approach to detect the suspicious u-serrated patterns from the detected ridge-endings. For each detected u-serrated pattern we provide a score that indicates the reliability of its detection. In order to evaluate the proposed approach, we created a data set with 180 DIF images for serration pattern analysis. This data set consists of seven subsets which were obtained from various biopsy samples under different conditions. We achieve an average recognition rate of 82.2% of the u-serrated pattern on these 180 DIF images, which is comparable to the recognition rate achieved by experienced medical doctors and pathologists.

Proposal for an eHealth Based Ecosystem Serving National Healthcare.

The European Union (EU)'s keen concern about citizens' health and well-being advancement has been expressed at all levels. It has been understood that at present, these can only be achieved through coordinated actions at the individual member states' level based on EU directives, as well as through promoting and funding R&D and expanding the use of eHealth technologies. Despite the diversities and particularities among member states, common values such as universal access to good quality healthcare, equity, and solidarity have been widely accepted across EU. That demanded the adoption of policies and follow directives, which streamlined actions to bridge healthcare gaps, and facilitate cross-border healthcare. This paper articulates a framework for deriving a national healthcare system, based on interoperable Electronic Health Record (EHR) with safeguarding healthcare quality, enabling quadruple helix (Public, Academia, Industry, NGOs) driven R&D and guided by a patient-centered approach. A methodology to develop an integrated EHR at National level is proposed as a prerequisite for eHealth and put into perspective. Recommendations are given for the steps needed, from the managerial, legal, technical, and financial concerns in developing an open access, patient-centered national healthcare system based on the context and constraints of a country. The example of a small country to apply the proposed methodology is demonstrated. Stakeholders, including citizens, healthcare professionals, academia, and the industry are mobilized, enabled, and incentivized for implementing the methodology. Experiences are aspired to be offered as lessons learned for other countries to adapt on their environment.

Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest.

The weight of a pig and the rate of its growth are key elements in pig production. In particular, predicting future growth is extremely useful, since it can help in determining feed costs, pen space requirements, and the age at which a pig reaches a desired slaughter weight. However, making these predictions is challenging, due to the natural variation in how individual pigs grow, and the different causes of this variation. In this paper, we used machine learning, namely random forest (RF) regression, for predicting the age at which the slaughter weight of 120 kg is reached. Additionally, we used the variable importance score from RF to quantify the importance of different types of input data for that prediction. Data of 32,979 purebred Large White pigs were provided by Topigs Norsvin, consisting of phenotypic data, estimated breeding values (EBVs), along with pedigree and pedigree-genetic relationships. Moreover, we presented a 2-step data reduction procedure, based on random projections (RPs) and principal component analysis (PCA), to extract features from the pedigree and genetic similarity matrices for use as inputs in the prediction models. Our results showed that relevant phenotypic features were the most effective in predicting the output (age at 120 kg), explaining approximately 62% of its variance (i.e., R2 = 0.62). Estimated breeding value, pedigree, or pedigree-genetic features interchangeably explain 2% of additional variance when added to the phenotypic features, while explaining, respectively, 38%, 39%, and 34% of the variance when used separately.

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study.

BACKGROUND: Each year, approximately 800,000 people die by suicide worldwide, accounting for 1-2 in every 100 deaths. It is always a tragic event with a huge impact on family, friends, the community and health professionals. Unfortunately, suicide prevention and the development of risk assessment tools have been hindered by the complexity of the underlying mechanisms and the dynamic nature of a person's motivation and intent. Many of those who die by suicide had contact with health services in the preceding year but identifying those most at risk remains a challenge. OBJECTIVE: To explore the feasibility of using artificial neural networks with routinely collected electronic health records to support the identification of those at high risk of suicide when in contact with health services. METHODS: Using the Secure Anonymised Information Linkage Databank UK, we extracted the data of those who died by suicide between 2001 and 2015 and paired controls. Looking at primary (general practice) and secondary (hospital admissions) electronic health records, we built a binary feature vector coding the presence of risk factors at different times prior to death. Risk factors included: general practice contact and hospital admission; diagnosis of mental health issues; injury and poisoning; substance misuse; maltreatment; sleep disorders; and the prescription of opiates and psychotropics. Basic artificial neural networks were trained to differentiate between the suicide cases and paired controls. We interpreted the output score as the estimated suicide risk. System performance was assessed with 10x10-fold repeated cross-validation, and its behavior was studied by representing the distribution of estimated risk across the cases and controls, and the distribution of factors across estimated risks. RESULTS: We extracted a total of 2604 suicide cases and 20 paired controls per case. Our best system attained a mean error rate of 26.78% (SD 1.46; 64.57% of sensitivity and 81.86% of specificity). While the distribution of controls was concentrated around estimated risks < 0.5, cases were almost uniformly distributed between 0 and 1. Prescription of psychotropics, depression and anxiety, and self-harm increased the estimated risk by ~0.4. At least 95% of those presenting these factors were identified as suicide cases. CONCLUSIONS: Despite the simplicity of the implemented system, the proposed methodology obtained an accuracy like other published methods based on specialized questionnaire generated data. Most of the errors came from the heterogeneity of patterns shown by suicide cases, some of which were identical to those of the paired controls. Prescription of psychotropics, depression and anxiety, and self-harm were strongly linked with higher estimated risk scores, followed by hospital admission and long-term drug and alcohol misuse. Other risk factors like sleep disorders and maltreatment had more complex effects.

Automating Vector Autoregression on Electronic Patient Diary Data.

Finding the best vector autoregression model for any dataset, medical or otherwise, is a process that, to this day, is frequently performed manually in an iterative manner requiring a statistical expertize and time. Very few software solutions for automating this process exist, and they still require statistical expertize to operate. We propose a new application called Autovar, for the automation of finding vector autoregression models for time series data. The approach closely resembles the way in which experts work manually. Our proposal offers improvements over the manual approach by leveraging computing power, e.g., by considering multiple alternatives instead of choosing just one. In this paper, we describe the design and implementation of Autovar, we compare its performance against experts working manually, and we compare its features to those of the most used commercial solution available today. The main contribution of Autovar is to show that vector autoregression on a large scale is feasible. We show that an exhaustive approach for model selection can be relatively safe to use. This study forms an important step toward making adaptive, personalized treatment available and affordable for all branches of healthcare.

Integrated Electronic Health Record Database Management System: A Proposal.

eHealth has attained significant importance as a new mechanism for health management and medical practice. However, the technological growth of eHealth is still limited by technical expertise needed to develop appropriate products. Researchers are constantly in a process of developing and testing new software for building and handling Clinical Medical Records, being renamed to Electronic Health Record (EHR) systems; EHRs take full advantage of the technological developments and at the same time provide increased diagnostic and treatment capabilities to doctors. A step to be considered for facilitating this aim is to involve more actively the doctor in building the fundamental steps for creating the EHR system and database. A global clinical patient record database management system can be electronically created by simulating real life medical practice health record taking and utilizing, analyzing the recorded parameters. This proposed approach demonstrates the effective implementation of a universal classic medical record in electronic form, a procedure by which, clinicians are led to utilize algorithms and intelligent systems for their differential diagnosis, final diagnosis and treatment strategies.

Trainable COSFIRE filters for vessel delineation with application to retinal images.

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se=0.7655, Sp=0.9704; STARE: Se=0.7716, Sp=0.9701; CHASE_DB1: Se=0.7585, Sp=0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.

Contextual modulation as de-texturizer.

Contextual modulation refers to the effect of texture placed outside of a neuron's classical receptive field as well as the effect of surround texture on the perceptual properties of variegated regions within. In this minireview, we argue that one role of contextual modulation is to enhance the perception of contours at the expense of textures, in short to de-texturize the image. The evidence for this role comes mainly from three sources: psychophysical studies of shape after-effects, computational models of neurons that exhibit iso-orientation surround inhibition, and fMRI studies revealing specialized areas for contour as opposed to texture processing. The relationship between psychophysical studies that support the notion of contextual modulation as de-texturizer and those that investigate contour integration and crowding is discussed.

Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models.

The remarkable abilities of the primate visual system have inspired the construction of computational models of some visual neurons. We propose a trainable hierarchical object recognition model, which we call S-COSFIRE (S stands for Shape and COSFIRE stands for Combination Of Shifted FIlter REsponses) and use it to localize and recognize objects of interests embedded in complex scenes. It is inspired by the visual processing in the ventral stream (V1/V2 → V4 → TEO). Recognition and localization of objects embedded in complex scenes is important for many computer vision applications. Most existing methods require prior segmentation of the objects from the background which on its turn requires recognition. An S-COSFIRE filter is automatically configured to be selective for an arrangement of contour-based features that belong to a prototype shape specified by an example. The configuration comprises selecting relevant vertex detectors and determining certain blur and shift parameters. The response is computed as the weighted geometric mean of the blurred and shifted responses of the selected vertex detectors. S-COSFIRE filters share similar properties with some neurons in inferotemporal cortex, which provided inspiration for this work. We demonstrate the effectiveness of S-COSFIRE filters in two applications: letter and keyword spotting in handwritten manuscripts and object spotting in complex scenes for the computer vision system of a domestic robot. S-COSFIRE filters are effective to recognize and localize (deformable) objects in images of complex scenes without requiring prior segmentation. They are versatile trainable shape detectors, conceptually simple and easy to implement. The presented hierarchical shape representation contributes to a better understanding of the brain and to more robust computer vision algorithms.

A push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detection.

We propose a computational model of a simple cell with push-pull inhibition, a property that is observed in many real simple cells. It is based on an existing model called Combination of Receptive Fields or CORF for brevity. A CORF model uses as afferent inputs the responses of model LGN cells with appropriately aligned center-surround receptive fields, and combines their output with a weighted geometric mean. The output of the proposed model simple cell with push-pull inhibition, which we call push-pull CORF, is computed as the response of a CORF model cell that is selective for a stimulus with preferred orientation and preferred contrast minus a fraction of the response of a CORF model cell that responds to the same stimulus but of opposite contrast. We demonstrate that the proposed push-pull CORF model improves signal-to-noise ratio (SNR) and achieves further properties that are observed in real simple cells, namely separability of spatial frequency and orientation as well as contrast-dependent changes in spatial frequency tuning. We also demonstrate the effectiveness of the proposed push-pull CORF model in contour detection, which is believed to be the primary biological role of simple cells. We use the RuG (40 images) and Berkeley (500 images) benchmark data sets of images with natural scenes and show that the proposed model outperforms, with very high statistical significance, the basic CORF model without inhibition, Gabor-based models with isotropic surround inhibition, and the Canny edge detector. The push-pull CORF model that we propose is a contribution to a better understanding of how visual information is processed in the brain as it provides the ability to reproduce a wider range of properties exhibited by real simple cells. As a result of push-pull inhibition a CORF model exhibits an improved SNR, which is the reason for a more effective contour detection.

Generating personalized advice for schizophrenia patients.

UNLABELLED: The results of routine patient assessments in psychiatric healthcare in the Northern Netherlands are primarily used to support clinicians. We developed Wegweis, a web-based advice platform, to make this data accessible and understandable for patients. OBJECTIVE: We show that a fully automated explanation and interpretation of assessment results for schizophrenia patients, which prioritizes the information in the same way that a clinician would, is possible and is considered helpful and relevant by patients. The goal is not to replace the clinician but rather to function as a second perspective and to enable patient empowerment through knowledge. METHODS: We have developed and implemented an ontology-based approach for selecting and ranking information for schizophrenia patients based on their routine assessment results. Our approach ranks information by severity of associated schizophrenia-related problems and uses an ontology to decouple problems from advice, which adds robustness to the system, because advice can be inferred for problems that have no exact match. RESULTS: We created a problem ontology, validated by a group of experts, to combine and interpret the results of multiple schizophrenia-specific questionnaires. We designed and implemented a novel ontology-based algorithm for ranking and selecting advice, based on questionnaire answers. We designed, implemented, and illustrated Wegweis, a proof of concept for our algorithm, and, to the best of our knowledge, the first fully automated interpretation of assessment results for patients suffering from schizophrenia. We evaluated the system vis-à-vis the opinions of clinicians and patients in two experiments. For the task of identifying important problems based on MANSA questionnaires (the MANSA is a satisfaction questionnaire commonly used in schizophrenia assessments), our system corresponds to the opinion of clinicians 94% of the time for the first three problems and 72% of the time, overall. Patients find two out of the first three advice topics selected by the system to be relevant and roughly half of the advice topics overall. CONCLUSIONS: Our findings suggest that an approach that uses problem severities to identify important problems for a patient corresponds closely to the way a clinician thinks. Furthermore, after applying a severity threshold, the majority of advice units selected by the system are considered relevant by the patients. Our findings pave the way for the development of systems that facilitate patient-centered care for chronic illnesses by automating the sharing of assessment results between patient and clinician.

Discriminative power of visual attributes in dermatology.

BACKGROUND/PURPOSE: Visual characteristics such as color and shape of skin lesions play an important role in the diagnostic process. In this contribution, we quantify the discriminative power of such attributes using an information theoretical approach. METHODS: We estimate the probability of occurrence of each attribute as a function of the skin diseases. We use the distribution of this probability across the studied diseases and its entropy to define the discriminative power of the attribute. The discriminative power has a maximum value for attributes that occur (or do not occur) for only one disease and a minimum value for those which are equally likely to be observed among all diseases. RESULTS: Verrucous surface, red and brown colors, and the presence of more than 10 lesions are among the most informative attributes. A ranking of attributes is also carried out and used together with a naive Bayesian classifier, yielding results that confirm the soundness of the proposed method. CONCLUSION: proposed measure is proven to be a reliable way of assessing the discriminative power of dermatological attributes, and it also helps generate a condensed dermatological lexicon. Therefore, it can be of added value to the manual or computer-aided diagnostic process.

Trainable COSFIRE filters for keypoint detection and pattern recognition.

BACKGROUND: Keypoint detection is important for many computer vision applications. Existing methods suffer from insufficient selectivity regarding the shape properties of features and are vulnerable to contrast variations and to the presence of noise or texture. METHODS: We propose a trainable filter which we call Combination Of Shifted FIlter REsponses (COSFIRE) and use for keypoint detection and pattern recognition. It is automatically configured to be selective for a local contour pattern specified by an example. The configuration comprises selecting given channels of a bank of Gabor filters and determining certain blur and shift parameters. A COSFIRE filter response is computed as the weighted geometric mean of the blurred and shifted responses of the selected Gabor filters. It shares similar properties with some shape-selective neurons in visual cortex, which provided inspiration for this work. RESULTS: We demonstrate the effectiveness of the proposed filters in three applications: the detection of retinal vascular bifurcations (DRIVE dataset: 98.50 percent recall, 96.09 percent precision), the recognition of handwritten digits (MNIST dataset: 99.48 percent correct classification), and the detection and recognition of traffic signs in complex scenes (100 percent recall and precision). CONCLUSIONS: The proposed COSFIRE filters are conceptually simple and easy to implement. They are versatile keypoint detectors and are highly effective in practical computer vision applications.

A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model.

Simple cells in primary visual cortex are believed to extract local contour information from a visual scene. The 2D Gabor function (GF) model has gained particular popularity as a computational model of a simple cell. However, it short-cuts the LGN, it cannot reproduce a number of properties of real simple cells, and its effectiveness in contour detection tasks has never been compared with the effectiveness of alternative models. We propose a computational model that uses as afferent inputs the responses of model LGN cells with center-surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances of the CORF and GF models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths. The RF map of the proposed CORF model, determined with simulated reverse correlation, can be divided in elongated excitatory and inhibitory regions typical of simple cells. The modulated response to shifted gratings that this model shows is also characteristic of a simple cell. Furthermore, the CORF model exhibits cross orientation suppression, contrast invariant orientation tuning and response saturation. These properties are observed in real simple cells, but are not possessed by the GF model. The proposed CORF model outperforms the GF model in contour detection with high statistical confidence (RuG data set: p<10(-4), and Berkeley data set: p<10(-4)). The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells.