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Clothing and fashion are an integral part of our everyday lives. In this paper we present an approach to studying fashion both on the runway and in more real-world settings, computationally, and at large scale, using computer vision. Our contributions include collecting a new runway dataset, designing features suitable for capturing outfit appearance, collecting human judgments of outfit similarity,...
We propose a new categorical object recognition algorithm robust to scale changes. We first partition an input image into k regions by using depth data from an RGB-D sensor, and then we estimate the object scale for each partitioned region. Finally, scaled model is applied to recognize the object.
This paper presents a sonification model to convert object tracking information into sound in real time. The goal is to generate a sound that describes the information given by a trajectory - such as position, direction, velocity and shape - to help visually impaired people to "see" the world: how can we describe to them a square like we do by drawing it in a sheet of paper? The usage can...
Vehicle classification system is an important part of intelligent transportation system (ITS), which can provide us the necessary information for autonomous navigation, toll systems, surveillance and security systems, and transport planning. In this paper, we introduce a vehicle classification system based on dynamic Bayesian network (DBN). Three main types of features are employed in our system:...
With the increasing needs from a variety of real world applications like context retrieval and aid reading, scene text recognition is attracting more and more attention from the computer vision community. Scene text character (STC) recognition plays an important role in this task. However, recognition of STC is a challenging task due to a series of problems, like different illumination conditions,...
In this paper, robot control by human motion data is considered. The conventional motion reproduction structure had its limitation on the performance due to the lack of means for environmental sensing. This study implements the widely used vision based approach. The possible reproduction structure with both visual and tactile senses are discussed. The proposed reproduction structure with both visual...
In this paper a new perspective is provided on the re-ranking problem, which is essential in pattern recognition and computer vision tasks. Items are efficiently organized using the minimal spanning tree (MST) and the orthogonal-MST graph and their similarity is calculated through an appropriate graph traversal method. The graph is augmented consecutively providing alternative paths, however not escaping...
Human action recognition has been one of the most challenging topics in computer vision during the last decade. This paper presents a novel approach for recognizing view independent human actions based on analysis of Fourier transform and Radon transform of self similarity matrix of features obtained from the action. The proposed feature descriptor is extracted from human point cloud over the time...
Shape retrieval is still a challenging problem. To address this problem, there is a growing interest in context-sensitive shape retrieval. In such methods, when computing the similarity between any two shape objects, the influence of their neighbors is propagated by a diffusion process on contextual space. We can re-evaluate the pairwise similarities to get better retrieval results. Although the context-sensitive...
In this paper, we present a novel generalized Segment-Forest Model (SFM) to segment an object as well as label all the object's semantic parts simultaneously. Segment-Forest is composed by various generated segment trees that act directly on super pixels. Unlike recent works, SFM does not need the prior information like skeleton to capture the core structure of an object, but actively learns the structure...
In this paper, we present a pedestrian tracking system by using image segmentation algorithm, which incorporated pedestrian shape prior into Random Walks segmentation [1] from a static image, and tracking people by Connected Component Labeling Algorithm. We improve the random walks segmentation algorithm by using prior shape information, which provides appropriate seeds for the pedestrian segmentation...
To understand the human action in still images, it is effective to detect the human region. However, since appearance of human is much different due to pose and occlusion, the detection is quite difficult. Here we propose robust human detection method to pose and occlusion using Bag-of-Words (BoW). In general, the location information is helpful in classification. When the human has occlusion and...
As an increasing number of digital images are generated, a demand for an efficient and effective image retrieval mechanisms grows. In this work, we present a new skeleton-based shape retrieval algorithm, which starts by drawing circles of increasing radius around skeleton points. Since each skeleton corresponds to the center of a maximally inscribed circle, this process results in circles that are...
3D object recognition from 3D scenes, is one of the challenges of several researchers in the field of computer vision, engineering and Robotics. The occlusion is one of the problems that we can found. One of the possible solutions in this situation is to find a part of an object in the scene that can be identified. For this reason, we are mainly interested to partial shape retrieval methods. In this...
Shape context is a classic shape retrieval method with translation invariance, but it has not scalar or rotational invariance, which limits its application. A new shape feature descriptor-centroid distance ratio (CdR) is proposed and an improved shape retrieval algorithm based on CdR and shape context is brought forth. First, the contour points are sampled in order to reduce computation and then,...
In this paper, a novel approach is proposed for shape classification based on the semi-supervised framework. For shape similarity-measuring problem, in order to avoid solving an NP problem produced by finding some affine transformation and to enhance its robustness for local changes of the shapes, we switch to compute an energy index defined by the degree of segmentation. The corresponding segmentation...
In this paper, we propose a framework to discover and segment favorite object from the natural images. The main idea is to first generate the shape based common template of the favorite object using the images collected from the web. Then, the common template is used to extract the favorite object from the original images. In the common template generation, co-segmentation is used to provide the initial...
Traffic light is one of the important signs for drivers that help managing the car flow and reducing accident on the road. As of today technology, there exists a traffic light detection system that warns the driver to reduce the accident significantly. In this paper, we are concerned with only the red and yellow traffic light to reduce false positive and time consumption. The fast radial symmetry...
Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing...
Craniosynostosis, a disorder in which one or more fibrous joints of the skull fuse prematurely, causes skull deformity and is associated with increased intracranial pressure and developmental delays. Although clinicians can easily diagnose craniosynostosis and can classify its type, being able to quantify the condition is an important problem in craniofacial research. While several papers have attempted...
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