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In the field of electronics device assembly, miniaturization of components, denser packing of boards, surface mounting technology, and highly automated assembly equipment make the task of inspecting the defects of soldering joints in the electronics products more critical and more difficult for humans. The automated inspection systems are required for the stable inspection of products. One of the...
This work describes a robot visual homing model that employs, for the first time, the conjugate gradient Temporal Difference (TD-conj) method. TD-conj was proved to be equivalent to a gradient TD method with a variable λ, denoted as (TD(λt(conj))), when both are used with function approximation techniques. This fact is employed in the model to improve its performance. Based on visual input that is...
We propose a multiclass hierarchical abductive learning classifier and apply it to improve the recognition rate of handwritten numerals while reduce the dimensionality of the feature space. For handwritten recognition, there are ten classes. Using 9 binary GMDH-based neural network models structured in a hierarchy has led to improving balance factor of the dataset for each classifier and improving...
The visual cortex contains well-ordered maps for visual features such as orientation. Existing computational approaches have demonstrated that biologically realistic global organization can be obtained. However, specific local properties such as variability in orientation tuning have not been thoroughly investigated. The purpose of this paper is to provide a computationally grounded interpretation...
Non-linearity of color changing in various lighting conditions is one of the primary factors which make lane color recognition difficult. This paper introduces an illumination invariant lane color recognition method which can recognize two lane colors (white, yellow) and copes with the non-linearity by using neural networks. Our method utilizes the road texture as the indicator of illumination condition...
This paper introduces a new supervised Bayesian approach to hyper-spectral image segmentation. The algorithm mainly consists of two steps: (a) learning, for each class label, the posterior probability distributions, based on a multinomial logistic regression model; (b) segmenting the hyperspectral image, based on the posterior probability distribution of the image of class labels built on the learned...
We describe a proof of concept for class knowledge transfer from a labeled hyperspectral image to an unlabeled image, captured with a different (hyper-/multi-spectral) sensor, when the spatial extents of the images partially overlap. By defining a set of spatio-spectral correspondences between the labeled source image and the unlabeled target image, we create a mapping between the images we can use...
Standard multiple instance learning (MIL) techniques are capable of learning when there is a lack of target information (including size, shape, and even location); however, this is attained at the cost of the utility of spatial information. This is unfortunate because in many image analysis applications, there is a substantial amount of observable spatial information. The research presented in the...
License Plate Recognition (LPR) is a very important research topic in computer vision of ITS. License plate location is the key step of LPR. Though numerous of techniques have been developed, most approaches work only under restricted conditions such as fixed illumination, limited vehicle license plates,and simple backgrounds. This paper attempts to use the AdaBoost algorithm to build up classifiers...
Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies...
This paper studies the problem of learning a full range of pairwise affinities gained by integrating local grouping cues for spectral segmentation. The overall quality of the spectral segmentation depends mainly on the pairwise pixel affinities. By employing a semi-supervised learning technique, optimal affinities are learnt from the test image without iteration. We first construct a multi-layer graph...
In this paper, we develop an automatic facial expression recognition system which establishes relations between facial expressions and the facial parts changes. Here, the differences between neutral and emotional states are used to help locating and identifying the essential facial parts for human expressions. For face description, region-based method to compute LBP features is applied then the most...
Support vector machines (SVM) is gaining a considerable attention as an approach to improvement performance of the content-based image retrieval (CBIR). Most SVM for CBIR rely on global feature, which length of the feature representation is fixed. However, region-based image retrieval (RBIR) use variable length representation, and common kernel utilize the inner product or lp norm in input space,...
Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation...
We address the task of learning a semantic segmentation from weakly supervised data. Our aim is to devise a system that predicts an object label for each pixel by making use of only image level labels during training - the information whether a certain object is present or not in the image. Such coarse tagging of images is faster and easier to obtain as opposed to the tedious task of pixelwise labeling...
In this paper, we present a new perceptual grouping algorithm using sparse semi-supervised learning (SSSL). In SSSL, KD-tree is used for effective representation and efficient retrieval. SSSL performs both transductive and inductive inference with a new dynamic graph concept. The perceptual grouping problem is tackled using SSSL to group different patterns into one object and separate similar patterns...
This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given a limited amount of hand labelled pixels, typically the start and end frames of a chunk of video, an EM based algorithm propagates labels through the rest of the frames of the video sequence. As a result, the user obtains pixelwise labelled...
In this paper we present a novel class of so-called Radon-Like features, which allow for aggregation of spatially distributed image statistics into compact feature descriptors. Radon-Like features, which can be efficiently computed, lend themselves for use with both supervised and unsupervised learning methods. Here we describe various instantiations of these features and demonstrate there usefulness...
Conditional Random Field models have proved effective for several low-level computer vision problems. Inference in these models involves solving a combinatorial optimization problem, with methods such as graph cuts, belief propagation. Although several methods have been proposed to learn the model parameters from training data, they suffer from various drawbacks. Learning these parameters involves...
Human posture recognition is gaining increasing attention in the fields of artificial intelligence and computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more comprehensive problem of video sequence...
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