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Several methods are available in computer vision to recognize objects. Mostly, these methods consist of two well-separated parts: first the region of interest has to be recognized on the input image. After this, the visual representation of the object has to be compared with already-known samples: there are several ways, mostly based on the shape, color and pattern, or corner- or keypoints of the...
This paper introduces a method to guide the visual search towards a searched object, analogously to what is performed by the top-down visual attention mechanism. This is done by prioritizing scene descriptors based on their Hamming distance to the descriptors of the target. The proposal has constant space and time complexity in relation to the number of descriptors of the searched object. Moreover,...
This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i.e., without using any ground-truth bounding boxes for training. The key idea is to analyze the change in the recognition scores when artificially masking out different regions of...
In this paper, we investigate the ability of humans to recognize objects using different types of edges. Edges arise in images because of several different physical phenomena, such as shadow boundaries, changes in material albedo or reflectance, changes to surface normals, and occlusion boundaries. By constructing synthetic photo realistic scenes, we control which edges are visible in a rendered image...
This work describes the implementation of an object recognition service on top of energy and resource-constrained hardware. A complete pipeline for object recognition based on the BRISK visual features is implemented on Intel Imote2 sensor devices. The reference implementation is used to assess the performance of the object recognition pipeline in terms of processing time and recognition accuracy.
Although man has become sedentary over time, his wish to travel the world remains as strong as ever. The aim of this paper is to show how techniques based on imagery and Augmented Reality (AR) can prove to be of great help when discovering a new urban environment and observing the evolution of the natural environment. The study's support is naturally the Smartphone which in just a few years has become...
In the state-of-the-art visual object recognition, there are a number of descriptors that have been proposed for various visual recognition tasks. But it is still difficult to decide which descriptors have more significant impact on this task. The descriptors should be distinctive and at the same time robust to changes in viewing conditions. This paper evaluates the performance of two distinctive...
Local image features around interest-points have been widely used in order to exploit the similarities between different views of an object in different images. While there are numerous algorithms on detecting the interest-points and defining the local features, few have focused on the importance of the matching process. In this paper, we presented a method that matches interest-points detected via...
In this paper we present a method for learning new objects situated in uncontrolled and unstructured environments. Visual information only is usually not sufficient for a reliable segmentation and learning of unknown objects without any a priori information. We propose an approach in which the robot introduces additional information by manipulating the entities in the scene, thus generating sufficient...
This paper presents a new model for capturing spatial information for object categorization with bag-of-words (BOW). BOW models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the Spatial Pyramid Matching (SPM) technique is the most notable. We propose a new method...
In recent years several works have aimed at exploiting color information in order to improve the bag-of-words based image representation. There are two stages in which color information can be applied in the bag-of-words framework. Firstly, feature detection can be improved by choosing highly informative color-based regions. Secondly, feature description, typically focusing on shape, can be improved...
This work contributes to part-based object detection and recognition by introducing an enhanced method for local part detection. The method is based on complex-valued multiresolution Gabor features and their ranking using multiple hypothesis testing. In the present work, our main contribution is the introduction of a canonical object space, where objects are represented in their ``expected pose and...
This paper proposes a method for learning viewpoint detection models for object categories that facilitate sequential object category recognition and viewpoint planning. We have examined such models for several state-of-the-art object detection methods. Our learning procedure has been evaluated using an exhaustive multiview category database recently collected for multiview category recognition research...
In this paper a system for illuminated manuscripts images analysis is presented. In particular the bag-of-keypoints strategy, commonly adopted for object recognition, image classification and scene recognition, is applied to the classification of automatically extracted miniatures. Pictures are characterized by SURF descriptors, and a classification procedure is performed, comparing the results of...
In many cases, visual tracking is based on detecting, describing, and then matching local features. A variety of algorithms for these steps have been proposed and used in tracking systems, leading to an increased need for independent comparisons. However, existing evaluations are geared towards object recognition and image retrieval, and their results have limited validity for real-time visual tracking...
Visual category recognition is challenging in computer vision and has several problem. Some of problems on visual category recognition are variance to the object instance position and background clutter. In this paper, we propose method select region of interest (ROI) in training and recognizing automatically. This provide invariance to object instance position and removing background clutter. In...
This article presents a method aiming at quantifying the visual similarity between an image and a class model. This kind of problem is recurrent in many applications such as object recognition, image classification, etc. In this paper, we propose to label a self-organizing map (SOM) to measure image similarity. To manage this goal, we feed local signatures associated to the regions of interest into...
Visual dictionaries have been successfully applied to ??bags-of-points?? image representations for generic object recognition. Usually the choice of low-level interest region detector and region descriptor (channel) has significant impact on the performance of visual dictionaries. In this paper, we propose a discriminative evaluation method-Maximum Mutual Information (MMI) curves to analyze the properties...
This paper presents object-based image retrieval using a novel method based on perceptual grouping. The perceptual grouping is obtained by detecting the line edge from a square block using the two consecutive primitive edge differences detector. Object segmentation and recognition is the primary step of computer vision for applying for an image retrieval of higher-level image analysis. However, automatic...
Periodicity is at the core of the recognition of many actions. This paper takes the following steps to detect and measure periodicity. 1) We establish a conceptual framework of classifying periodicity in 10 essential cases, the most important of which are flashing (of a traffic light), pulsing (of an anemone), swinging (of wings), spinning (of a swimmer), turning (of a conductor), shuttling (of a...
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