1,163,570 research outputs found

    Compressively Sensed Image Recognition

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    Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201

    Place recognition: An Overview of Vision Perspective

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    Place recognition is one of the most fundamental topics in computer vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of wisdom accumulated in this field, place recognition still remains an open problem due to the various ways in which the appearance of real-world places may differ. This paper presents an overview of the place recognition literature. Since condition invariant and viewpoint invariant features are essential factors to long-term robust visual place recognition system, We start with traditional image description methodology developed in the past, which exploit techniques from image retrieval field. Recently, the rapid advances of related fields such as object detection and image classification have inspired a new technique to improve visual place recognition system, i.e., convolutional neural networks (CNNs). Thus we then introduce recent progress of visual place recognition system based on CNNs to automatically learn better image representations for places. Eventually, we close with discussions and future work of place recognition.Comment: Applied Sciences (2018

    Redundancy in Face Image Recognition

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    Many researchers paid attention to formulate different algorithms to faces and its classes for accurate classification but, did not paid attention to the fact that redundancy may exists even though faces with different classes are effectively classified. Researchers working on SVD and its extended algorithm versions which were based on face matrix decomposition for face recognition concluded that they are the best algorithms for classification of occluded faces. The problem with these designed algorithms is that there is every likely hood of having more than one value of amplification factor along with classified faces. It is pointed out by researchers that every face will be having one and only one amplification factor and its classified face. This factor will definitely add to the already existing facial recognition problems and challenges. Here is a paper which shows the redundancy in recognition which will be treated as an added problem and challenge for facial recognition
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