Pattern Recognition By a Scaled Corners Detection

In this paper we developed a new approach to extract points descriptor used for pattern recognition with Corner detection approach. We used scales of image, each scale was scaled by a scaling factor, detect the corners in each scale, extract the key points descriptor from these corners, and using these points descriptor as key features of recognition in the Hough Transform to classify the Descriptor to its class. We implemented and analyzed SIFT algorithm, corner detection algorithm, and the proposed approach. The experimental results using MATLAB of a proposed approach gave results of recognition with high accuracy. Keywords: Pattern Recognition; Corner Detection; SIFT; Hough Transform

A Spiking Neural Model of HT3D for Corner Detection

Obtaining good quality image features is of remarkable importance for most computer vision tasks. It has been demonstrated that the first layers of the human visual cortex are devoted to feature detection. The need for these features has made line, segment, and corner detection one of the most studied topics in computer vision. HT3D is a recent variant of the Hough transform for the combined detection of corners and line segments in images. It uses a 3D parameter space that enables the detection of segments instead of whole lines. This space also encloses canonical configurations of image corners, transforming corner detection into a pattern search problem. Spiking neural networks have previously been proposed for multiple image processing tasks, including corner and line detection using the Hough transform. Following these ideas, this paper presents and describes in detail a model to implement HT3D as a Spiking Neural Network for corner detection. The results obtained from a thorough testing of its implementation using real images evince the correctness of the Spiking Neural Network HT3D implementation. Such results are comparable to those obtained with the regular HT3D implementation, which are turn superior to other corner detection algorithms

Eye Corner Detection

Detection of corners of the eye is a good research topic. It plays an important role in multiple tasks performed in the field of Computer Vision. It also plays a key role in biometric systems. In this the- sis, initially, the existing corner detection methods are discussed. Using Hough transform line, circle and ellipse were found out in the given image. The proposed work includes, finding the eye region in the given face image using Template Matching method. Later on, we fit a rectangle to the matched eye region. And then, we find out the corners of the rectangle and approximate them to be the corners of the eye

A spiking neural model of HT3D for corner detection

La obtención de características de imagen de buena calidad es de notable importancia para la mayoría de las tareas de visión artificial. Se ha demostrado que las primeras capas de la corteza visual humana están dedicadas a la detección de características. La necesidad de estas características ha hecho que la detección de líneas, segmentos y esquinas sea uno de los temas más estudiados en la visión por computador. El HT3D es una variante reciente de la transformación Hough para la detección combinada de esquinas y segmentos de línea en imágenes. Utiliza un espacio de parámetros 3D que permite la detección de segmentos en lugar de líneas enteras. Este espacio también encierra configuraciones canónicas de las esquinas de la imagen, transformando la detección de esquinas en un problema de búsqueda de patrones. Las redes neuronales de picos (SNN) se han propuesto anteriormente para múltiples tareas de procesamiento de imágenes, incluyendo la detección de esquinas y líneas usando la transformación Hough. Siguiendo estas ideas, este documento presenta y describe en detalle un modelo para implementar el HT3D como una Red Neural de Picos (Spiking Neural Network) para la detección de esquinas. Los resultados obtenidos a partir de pruebas exhaustivas de su implementación utilizando imágenes reales evidencian la corrección de la implementación de la Red Neural Spiking HT3D. Tales resultados son comparables a los obtenidos con la implementación regular del HT3D, que a su vez son superiores a otros algoritmos de detección de esquinas.Obtaining good quality image features is of remarkable importance for most computer vision tasks. It has been demonstrated that the first layers of the human visual cortex are devoted to feature detection. The need for these features has made line, segment, and corner detection one of the most studied topics in computer vision. HT3D is a recent variant of the Hough transform for the combined detection of corners and line segments in images. It uses a 3D parameter space that enables the detection of segments instead of whole lines. This space also encloses canonical configurations of image corners, transforming corner detection into a pattern search problem. Spiking neural networks (SNN) have previously been proposed for multiple image processing tasks, including corner and line detection using the Hough transform. Following these ideas, this paper presents and describes in detail a model to implement HT3D as a Spiking Neural Network for corner detection. The results obtained froma thorough testing of its implementation using real images evince the correctness of the Spiking Neural Network HT3D implementation. Such results are comparable to those obtained with the regular HT3D implementation, which are in turn superior to other corner detection algorithms.• Unión Europea. Proyecto Interreg. Beca 0043_EUROAGE_4_E • Gobierno de España. Beca TIN2015-65686-C5-5-R • Junta de Extremadura. Beca GR15120 e IB16090peerReviewe

Points Descriptor in Pattern Recognition: A New Approach

We presented in the paper a new tactic, the first thing we have done is extracting the points of descriptor,which it is used in pattern recognition, especially in detection of corner algorithm. Scales of samples (images),each image is tuned by a factor (scale), collect the corners, and collect the points of descriptor key in thesecollected corners, in other words; Hough Transform uses the collected descriptors for classification process, andclassify each points of image to its equivalence class. Experimentally, by using MATLAB, we are shown highaccuracy of recognition result on the selected samples of objects

A variant of the Hough Transform for the combined detection of corners, segments, and polylines

The Hough Transform (HT) is an effective and popular technique for detecting image features such as lines and curves. From its standard form, numerous variants have emerged with the objective, in many cases, of extending the kind of image features that could be detected. Particularly, corner and line segment detection using HT has been separately addressed by several approaches. To deal with the combined detection of both image features (corners and segments), this paper presents a new variant of the Hough Transform. The proposed method provides an accurate detection of segment endpoints, even if they do not correspond to intersection points between line segments. Segments are detected from their endpoints, producing not only a set of isolated segments but also a collection of polylines. This provides a direct representation of the polygonal contours of the image despite imperfections in the input data such as missing or noisy feature points. It is also shown how this proposal can be extended to detect predefined polygonal shapes. The paper describes in detail every stage of the proposed method and includes experimental results obtained from real images showing the benefits of the proposal in comparison with other approaches

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201