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This paper presents an efficient VLSI implementation of a singular value decomposition (SVD) processor of on-line recursive independent component analysis (ORICA) for use in a real-time electroencephalography (EEG) system. ICA is a well-known method for blind source separation (BBS), which helps to obtain clear EEG signals without artifacts. In general, computations of ORICA are complicated and the...
Many combinatorial problems in fields such as object tracking involve reasoning over correspondence, e.g., calculating the probability that a measurement belongs to a particular track. Recent studies have shown that loopy belief propagation (LBP) provides a highly desirable option in the trade-off between accuracy and computational complexity in this task. LBP can be understood as a particular method...
Face recognition and verification is still a challenging problem due to several issues such as pose, facial expression, occlusion, imaging conditions, rotation, size and orientation. This paper addresses the problem of recognizing human faces despite the presence in pose and size variation. To handle these problems, we mainly focus on block size definition. Instead of uniform block we thus propose...
In digital communications, the non-linear phase characteristic of a digital communication channel must be equalized by using a phase-network in such a way that the whole communication channel has nearly linear-phase and does not distort the waveform of the transmitted signal. This paper proposes an improved iterative second-order-cone-programming (iSOCP) technique for designing an all-pass (AP) phase-network...
We present a framework for Tensor-based subspace Tracking via Kronecker-structured projections (TeTraKron). TeTraKron allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based subspace estimate. The latter can be computed via a structured projection applied to the matrix-based subspace estimate which enforces the multi-dimensional structure in a computationally efficient...
Pedestrian detection plays important roles in various applications such as automobile driving assistance and surveillance camera system. The co-occurrence histograms of oriented gradients (CoHOG) feature descriptor showed good performance since thirty co-occurrences at each pixel position represent various spatial characteristics of object shapes. Though extraction of co-occurrence histogram features...
The LANDMARC system is a radio frequency identification (RFID) based location system and has attracted great attention recently. In our implementation of the LANDMARC system, we have observed that some unusual large errors occur in the traditional LANDMARC. Thus, in this study, we proposed ways of resolving these problems. There are two major ideas proposed in this study. The first one is to forsake...
This paper proposes a text independent method for the classification of normal and pathological voices. If the classifier is text dependent i.e classifier is trained for a particular phoneme, then it may difficult for the patient to pronounce the particular phoneme. To overcome this difficulty, a text independent classification method is proposed, which uses Mel-Frequency Cepstral Coefficients (MFCCs)...
SenticNet 1.0 is one of the most widely used freely-available resources for concept-level opinion mining, containing about 5,700 common sense concepts and their corresponding polarity scores. Specific affective information associated to such concepts, however, is often desirable for tasks such as emotion recognition. In this work, we propose a method for assigning emotion labels to SenticNet concepts...
The early results from connectivity-based sensor network localization suffer from disappointing accuracy. The reason is partly due to the limited information of the problem, and also the deficiencies of the algorithms. This paper proposes a two-level range/indication of connectivity between each pair of nodes, which would indicate three levels of connectivity: strong, weak or nil. Theoretically, the...
Hashing methods have attracted much attention in large scale image research in recent years, because they are not only fast, but also needing a little memory. This paper proposed a balanced semi-supervised hashing method by dividing image into several blocks. With the help of improved semi-supervised hashing, we obtain a short hash code of each block, which jointed together forms a hash code of an...
Locality sensitive hashing (LSH) is quite popular in high dimensional data indexing. However, most of existing methods perform hashing in an unsupervised way, that is to say, hash functions are randomly generated without the prior information of the data. In this paper, we propose two improved LSH algorithms based on weakly supervised learning technique, which need only small quantities of labeled...
There have recently been many methods proposed for matching face sequences in the field of face retrieval. However, most of them have proven to be inefficient in large-scale video databases because they frequently require a huge amount of computational cost to obtain a high degree of accuracy. We present an efficient matching method that is based on the face sequences (called face tracks) in large-scale...
Although “Bag-of-Features” image models have shown very good potential for object matching and image retrieval, such a complex data representation requires computationally expensive similarity measure evaluation. In this paper, we propose a framework unifying dictionary-based and kernel-based similarity functions that highlights the tradeoff between powerful data representation and eff cient similarity...
Recently it has been shown that the performance of image set matching methods can be improved by clustering set samples into smaller and more coherent groups. Typically, set samples are treated independently during clustering, ie., clustering criteria have not been defined to exploit set characteristics. In this paper we introduce a novel approach to image set clustering by considering the similarities...
Different techniques exist for camera calibration based on photogrammetry or self-calibration. Much work has been done to obtain intrinsic and extrinsic camera parameters and also in data pre - and post-processing techniques. From a practical viewpoint, it is quite difficult to decide which calibration method produces the best results and even whether any data processing is necessary. This paper proposes...
In this paper we propose a new method for appearance-based pose estimation, called Local Procrustes Regression (LPR). In LPR, rather than learning a map between all available training samples and pose space, as is common for appearance-based pose estimation algorithms, the pose of an unknown sample is recovered locally from a small subset of the training samples, by utilizing their inter-point distances...
In this paper, we propose a novel method to implement fast detection of Common Visual Pattern (CVP). The purpose of CVP detection is to find the correspondences between the common visual regions of two given partial duplicate images. There are two major components of the proposed method which guarantee the good performance. First, we establish the Radiate-Geometric-Model (RGM). The RGM is represented...
Non-negative Matrix Factorization (NMF) is among the most popular subspace methods widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis criteria and achieves an efficient decomposition of the provided data to its discriminant parts has been proposed. However, this approach poses several limitations since it assumes...
Today many people in the world without any (or with little) knowledge about video recording, thanks to the widespread use of mobile devices (PDAs, mobile phones, etc.) take videos. However the unwanted movements of their hands typically blur and introduce disturbing jerkiness in the recorded sequences. A fundamental issue is the overall robustness with respect to different scene contents (indoor,...
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