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Similarity rank lists provide a method for learning generalization of classifiers from examples. Here, we apply it to invariant object recognition and demonstrate that it performs better than other approaches on view and illumination invariant recognition. Recognition from a single view reaches 87% success rate. To study its real world capabilities we introduce subsqare rank matching that works on...
Object recognition is a versatile capability. Automatic guided tours and augmented reality are just two examples. Humans seem to do it subconsciously — unaware of the extensive processing required for it — while it is a complex task for machines. Methods based on SIFT features have proven to be robust for recognition. However, a prior detection step is required to limit confusion, caused by, e.g.,...
In this paper, an efficient and automatic method for detection of multiple-objects of interest from images is presented. This method is based on using region similarity measures. The method starts by constructing two knowledge databases in which significant and distinctive textures extracted from both objects of interest and background are respectively represented. The proposed procedure continues...
Object detection and recognition are fundamental capabilities for a mobile robot. Objects are a powerful representation for a variety of tasks including mobile manipulation and inventory tracking. As a result, object-based world representations have seen a great deal of research interest in the last several years. However, these systems usually assume that object recognition is well-solved: they require...
We present a system for real-time general object recognition (gor) for indoor robot in complex scenes. A point cloud image containing the object to be recognized from a Kinect sensor, for general object at will, must be extracted a point cloud model of the object with the Cluster Extraction method, and then we can compute the global features of the object model, making up the model database after...
In this paper, we present an object recognition and pose estimation framework consisting of a novel global object descriptor, so called Viewpoint oriented Color-Shape Histogram (VCSH), which combines object's color and shape information. During the phase of object modeling and feature extraction, the whole object's color point cloud model is built by registration from multi-view color point clouds...
In this work we describe main features of software modules developed for Android smartphones that are dedicated for the blind users. The main module can recognise and match scanned objects to a database of objects., e.g. food or medicine containers. The two other modules are capable of detecting major colours and locate direction of the maximum brightness regions in the captured scenes. We conclude...
Object recognition methods usually rely on either structural or statistical description. These methods aim at describing different types of information such as the outer contour, the inner structure or texture effects. Comparing two objects then comes down to averaging different data representations which may be a tricky issue. In this paper, we introduce an object descriptor based on the spatial...
The paper discusses experiments (using exemplary classes of man-made objects) on the-same-class object detection based on the keypoint matching techniques. Two algorithms are used, i.e. building clusters of consistently similar and distributed keypoints, and matching individual points represented by novel descriptors incorporating semi-local geometry of images. It is shown that although detection...
Information Retrieval in large digital document repositories is at the same time a hard and crucial task. While the primary type of information available in documents is usually text, images play a very important role because they pictorially describe concepts that are dealt with in the document. Unfortunately, the semantic gap separating such a visual content from the underlying meaning is very wide...
In this paper, we analyze the effect of different image preprocessing techniques on the performance of Speeded Up Robust Features, SURF. We investigate the effects of the techniques like Histogram Equalization, Multiscale Retinex, and Image Adaptive Contrast Enhancement (IACE) scheme that we propose, on the SURF in terms of its feature points detection, and computational time for extracting the descriptors...
In this paper we propose a fast and robust descriptor for multiple view object recognition using a small number of training examples. In order to design a descriptor to be discriminative between many different object appearances, we base it on a combination of invariant color, edge and texture descriptors. We use a color descriptor based on a HSV histogram - as it is robust to size and position of...
This paper presents a system for object recognition and localization within unknown indoor environments. The system includes a GUI design through which the user may describe an object of interest by means of color, size, and shape. A novel coarse to fine identification mechanism that incorporates multiple views of an object is then used to locate the described object within an unknown environment...
This paper proposes a new framework for extracting facial features based on the bag of words method, and applies it to face and facial expression recognition. Recently, the bag of words method has been successfully used in object recognition. However, for recognition problems of facial images, the orderless collection of local patches in bag of words method cannot provide strongly distinctive information...
We propose a novel shape descriptor-Included Angle Histogram -for correspondence recovery of graphic vertex and shape-based object recognition. After detecting points local maximal curvature with and the center point of the contour, we construct vectors from the center point to the curvature points. Consequently the point descriptor can be obtained through computing the histograms of included-angles...
This paper addresses the problem of font retrieval using a query-by-example paradigm: given a font, retrieve the the most visually similar fonts. We describe a font by (a) rendering a set of reference characters, (b) extracting a feature vector for each reference character and (c) concatenating the-level character descriptors. The similarity between two fonts is simply the similarity between the vectorial...
In this work we address the problem of forest species recognition which is a very challenging task and has several potential applications in the wood industry. The first contribution of this work is a database composed of 22 different species of the Brazilian flora that has been carefully labeled by expert in wood anatomy. In addition, in this work we demonstrate through a series of comprehensive...
This paper presents a novel local feature descriptor, the Local Directional Pattern (LDP), for describing local image feature. A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy...
This paper addresses the problem of recognizing shadows from monochromatic natural images. Without chromatic information, shadow classification is very challenging because the invariant color cues are unavailable. Natural scenes make this problem even harder because of ambiguity from many near black objects. We propose to use both shadow-variant and shadow-invariant cues from illumination, textural...
Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only...
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