A DSmT-Based Approach for Data Association in the Context of Multiple Target Tracking

Kim, Tae Il; Bahn, Wook; Lee, Chang-Hun; Lee, Tae-jae; Jang, Byung-moon; Lee, Sanghoon; Moon, Min-Wug; Cho, Dong‐il Dan

doi:10.1007/978-3-642-33515-0_67

Cited by 3 publications

(1 citation statement)

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“…3.2.2 ALS with neural network (NN) algorithm. According to the above section, the ALS (Airouche et al, 2009) verified higher convergence performance, but it met few shortcomings in case of unpredictable velocities, computational complexity, lower efficiency, etc. Hence, we used the ALS method with a NN algorithm to enhance the performance of segmentation.…”

Section: Segmentationmentioning

confidence: 95%

An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

Rangayya¹,

Virupakshappa²,

Patil³

2021

IJICC

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PurposeOne of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.Design/methodology/approachThe proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.FindingsExperimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.Originality/valueThe good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.

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Section: Segmentationmentioning

confidence: 95%