The Experts below are selected from a list of 29190 Experts worldwide ranked by ideXlab platform

Tsungyung Hung - One of the best experts on this subject based on the ideXlab platform.

  • Local vector pattern in high order Derivative space for face recognition
    International Conference on Image Processing, 2014
    Co-Authors: Tsungyung Hung, Kuochin Fan
    Abstract:

    In this paper, a novel Local pattern descriptor generated by the proposed Local vector pattern (LVP) in high-order Derivative space is presented for face recognition. The proposed vector representation of the referenced pixel is generated to provide the one-dimensional structure of micropatterns. To effectively extract more detailed discriminative information in a given sub-region, the vector of LVP is refined by varying Local Derivative directions from the nth-order LVP in (n−1)th-order Derivative space. The proposed LVP is compared with the existing Local pattern descriptors including Local binary pattern (LBP), Local Derivative pattern (LDP), and Local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. Extensive experiments conducting on benchmark face image databases, FERET and Extended Yale B, demonstrate that the proposed LVP in high-order Derivative space indeed performs much better than LBP, LDP and LTrP for face recognition.

  • a novel Local pattern descriptor Local vector pattern in high order Derivative space for face recognition
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Kuochin Fan, Tsungyung Hung
    Abstract:

    In this paper, a novel Local pattern descriptor generated by the proposed Local vector pattern (LVP) in high-order Derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying Local Derivative directions from the \(n\) th-order LVP in \((n-1)\) th-order Derivative space, which is a much more resilient structure of micropatterns than standard Local pattern descriptors. The proposed LVP is compared with the existing Local pattern descriptors including Local binary pattern (LBP), Local Derivative pattern (LDP), and Local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in high-order Derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.

Subrahmanyam Murala - One of the best experts on this subject based on the ideXlab platform.

  • Local ternary co occurrence patterns a new feature descriptor for mri and ct image retrieval
    Neurocomputing, 2013
    Co-Authors: Subrahmanyam Murala
    Abstract:

    This paper presents a novel feature extraction algorithm called Local ternary co-occurrence patterns (LTCoP) for biomedical image retrieval. The LTCoP encodes the co-occurrence of similar ternary edges which are calculated based on the gray values of center pixel and its surrounding neighbors. Whereas the standard Local Derivative pattern (LDP) encodes the co-occurrence between the first-order Derivatives in a specific direction. The existing LDP is a specific direction rotational variant feature where as our method is rotational invariant. In addition, the effectiveness of our algorithm is confirmed by combining it with the Gabor transform. To prove the effectiveness of our algorithm, three experiments have been carried out on three different biomedical image databases. Out of which two are meant for computer tomography (CT) and one for magnetic resonance (MR) image retrieval. It is further mentioned that the database considered for three experiments are OASIS-MRI database, NEMA-CT database and VIA/I-ELCAP database which includes region of interest CT images. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, LTP, Local tetra patterns (LTrP) and LDP with and without Gabor transform.

  • Local tetra patterns a new feature descriptor for content based image retrieval
    IEEE Transactions on Image Processing, 2012
    Co-Authors: Subrahmanyam Murala, R P Maheshwari, R Balasubramanian
    Abstract:

    In this paper, we propose a novel image indexing and retrieval algorithm using Local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard Local binary pattern (LBP) and Local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference. The proposed method encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order Derivatives in vertical and horizontal directions. In addition, we propose a generic strategy to compute nth-order LTrP using (n - 1)th-order horizontal and vertical Derivatives for efficient CBIR and analyze the effectiveness of our proposed algorithm by combining it with the Gabor transform. The performance of the proposed method is compared with the LBP, the Local Derivative patterns, and the LTP based on the results obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2), and MIT VisTex database (DB3). Performance analysis shows that the proposed method improves the retrieval result from 70.34%/44.9% to 75.9%/48.7% in terms of average precision/average recall on database DB1, and from 79.97% to 85.30% and 82.23% to 90.02% in terms of average retrieval rate on databases DB2 and DB3, respectively, as compared with the standard LBP.

Kuochin Fan - One of the best experts on this subject based on the ideXlab platform.

  • Local vector pattern in high order Derivative space for face recognition
    International Conference on Image Processing, 2014
    Co-Authors: Tsungyung Hung, Kuochin Fan
    Abstract:

    In this paper, a novel Local pattern descriptor generated by the proposed Local vector pattern (LVP) in high-order Derivative space is presented for face recognition. The proposed vector representation of the referenced pixel is generated to provide the one-dimensional structure of micropatterns. To effectively extract more detailed discriminative information in a given sub-region, the vector of LVP is refined by varying Local Derivative directions from the nth-order LVP in (n−1)th-order Derivative space. The proposed LVP is compared with the existing Local pattern descriptors including Local binary pattern (LBP), Local Derivative pattern (LDP), and Local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. Extensive experiments conducting on benchmark face image databases, FERET and Extended Yale B, demonstrate that the proposed LVP in high-order Derivative space indeed performs much better than LBP, LDP and LTrP for face recognition.

  • a novel Local pattern descriptor Local vector pattern in high order Derivative space for face recognition
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Kuochin Fan, Tsungyung Hung
    Abstract:

    In this paper, a novel Local pattern descriptor generated by the proposed Local vector pattern (LVP) in high-order Derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying Local Derivative directions from the \(n\) th-order LVP in \((n-1)\) th-order Derivative space, which is a much more resilient structure of micropatterns than standard Local pattern descriptors. The proposed LVP is compared with the existing Local pattern descriptors including Local binary pattern (LBP), Local Derivative pattern (LDP), and Local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in high-order Derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.

Changxin Gao - One of the best experts on this subject based on the ideXlab platform.

  • Local fractional order Derivative vector quantization pattern for face recognition
    Asian Conference on Computer Vision, 2016
    Co-Authors: Nong Sang, Changxin Gao
    Abstract:

    Previous works have shown that fractional order Derivative can give a better image description compared with conventional integral one in applications of edge detection, image segmentation, image restoration, and so on. Motivated by this conclusion, in this paper, we propose a novel Local image descriptor, Local fractional order Derivative vector quantization pattern (fVQP), based on image Local directional fractional order Derivative feature vector and vector quantization method for face recognition. Compared with image integral order Derivative information based Local binary pattern (LBP), Local Derivative pattern (LDP) and Local directional Derivative pattern (LDDP), our fVQP image descriptor has the advantages of better image recognition performance and robust to noise. Extensive experimental results conducted on four benchmark face databases demonstrate the superior performance of our fVQP compared with existing state-of-the-art descriptors for face recognition in terms of recognition rate.

Shahrel Azmin Suandi - One of the best experts on this subject based on the ideXlab platform.

  • finger vein recognition using Local line binary pattern
    Sensors, 2011
    Co-Authors: Bakhtiar Affendi Rosdi, Chai Wuh Shing, Shahrel Azmin Suandi
    Abstract:

    In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called Local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in Local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and Local Derivative pattern (LDP).