Palmprint Region of Interest Cropping Based on Moore-Neighbor Tracing Algorithm

Mandeel, Thulfiqar H.; Isa, Mohd Nazrin Md; Anwar, Said Amirul; Ngadiran, Ruzelita

doi:10.1007/s11220-018-0199-6

Cited by 13 publications

(10 citation statements)

References 16 publications

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“…These palms are divided randomly into two categories to represent the left and right palms by which each category has 189 palms. The ROIs for this database obtained using the method proposed in [31].…”

Section: Results and Experiments Setupmentioning

confidence: 99%

A multi-instance multi-sample palmprint identification system

Mandeel

Anwar

2019

IJEECS

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<span>The multibiometric recognition system considered more reliable than the unimodal biometric recognition system due to the addition of an extra information that increases the discrimination between the classes. In this paper, a multi-sample multi-instance biometric recognition system is proposed. The aim of the proposed system is to increase the robustness of the identification. the proposed system also addresses the overfitting to the train samples problem of a feature extraction algorithm, named 2-Dimensional Linear Discriminant analysis (2D-LDA). The samples in the proposed method are bootstrapped and the 2D-LDA performed on each group during the offline phase. While in the online phase, the tested class will be transformed into subspaces using different eigenvectors that obtained from different samplings, and the results matched with templates in the corresponding subspace. To evaluate the proposed method, two palmprint databases are used which are IIT Delhi Touchless Palmprint Database and PolyU palmprint database, and different rank-level fusion algorithms are investigated. The results of the proposed method show improvement in the identification rate.</span>

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Section: Results and Experiments Setupmentioning

confidence: 99%

A multi-instance multi-sample palmprint identification system

Mandeel

Anwar

2019

IJEECS

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“…First of all, boundaries of each cells are traced based on the connectivity's of white pixels from black pixels background. Moore-Neighbor tracing algorithm's modified version Jacob's stopping criteria [14], [15], [16] is applied that scans starting from left bottom left corner to each rows going upwards and again starting from leftmost column to right until stop from where it started If it can complete a loop, then it'll be traced as segmented boundary and quantified as cell .In the end, the final image in binary and RGB form will be displayed by plotted each outer shell of cells marked with green color.…”

Section: B Image Segmentationmentioning

confidence: 99%

Non-Hodgkin Type Lymphoma Cancer Cell Detection using Connected Components Labeling and Moments of Image

Pavel¹,

Shakir²,

Muhtasim³

et al. 2021

IJACSA

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Cancers are one of the deadliest diseases with a costly treatment system in the world at present. In this paper a cost-effective, autonomous system of cancer-cell detection was proposed using several efficient image processing methods to develop an early stage non-Hodgkin type lymphoma which is a type of blood cancer. The system is implemented automatically to detect the traits of cancer in microscopy images of biopsy samples. Recent attempts have previously lacked flexibility in characteristics and the accuracy level is not consistent with the individual cancer type. The framework consisted three stages for detecting cancer on the basis of various detected traits including cell segmentation, quantification, area measurement analysis of cells, a center clump detection using the moment of image, identification of 4-connected components and Moore-Neighbor tracing algorithm. This methodology has been used in several sets of images and Feedback from these test executions has been used to improve the system. Subsequently, the proposed method can be used efficiently for used for autonomous non-hodgking type lymphoma cancer cell detection, which has an accuracy of 93.75%.

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“…Based on visual contrast, Karczmarek et al [11] designed a fuzzy saliency growth method to detect the salient part of the scene. Mandeel et al [12] developed a global and spatial region contrast algorithm to extract the region of interest (ROI), and obtained a full resolution saliency map through histogram-based method, which computes the Euclidean distance between color histograms of image blocks. Chen and Chu [13] proposed the spectral residual method to extract the salient part of the image: the redundant part was approximated by the local average filter and the logarithmic spectral filter, and removed from the input image.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Step 4. If ℎ ≤ , calculate the output weight by formula (11); otherwise, calculate the output weight by formula (12).…”

Section: = [mentioning

confidence: 99%

A Salient Object Detection Algorithm Based on Hierarchical Cognitive Mechanism

Tang¹,

Zeng²,

Ding³

et al. 2020

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The key of salient object detection is to extract the most attractive area in the scene. This paper fully explores the hierarchical cognitive mechanism of visual information, combines color contrast and depth contrast, and puts forward a salient object detection algorithm for the RGB-D image. Three saliency maps were prepared, namely, initial saliency map, middle saliency map and advanced saliency map. The three maps were then fused into a final saliency map. The proposed method was compared with six popular salient object detection methods on three RGB-D image datasets. The comparison shows that our algorithm achieved the best results in accuracy, recall rate and F-value. The research findings shed important new light on salient object detection in RGB-D images.

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Palmprint Region of Interest Cropping Based on Moore-Neighbor Tracing Algorithm

Cited by 13 publications

References 16 publications

A multi-instance multi-sample palmprint identification system

A multi-instance multi-sample palmprint identification system

Non-Hodgkin Type Lymphoma Cancer Cell Detection using Connected Components Labeling and Moments of Image

A Salient Object Detection Algorithm Based on Hierarchical Cognitive Mechanism

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