Adaptive Edge-guided Block-matching and 3D filtering (BM3D) Image Denoising Algorithm

Image denoising is a well studied field, yet reducing noise from images is still a valid challenge. Recently proposed Block-matching and 3D filtering (BM3D) is the current state of the art algorithm for denoising images corrupted by Additive White Gaussian noise (AWGN). Though BM3D outperforms all existing methods for AWGN denoising, still its performance decreases as the noise level increases in images, since it is harder to find proper match for reference blocks in the presence of highly corrupted pixel values. It also blurs sharp edges and textures. To overcome these problems we proposed an edge guided BM3D with selective pixel restoration. For higher noise levels it is possible to detect noisy pixels form its neighborhoods gray level statistics. We exploited this property to reduce noise as much as possible by applying a pre-filter. We also introduced an edge guided pixel restoration process in the hard-thresholding step of BM3D to restore the sharpness of edges and textures. Experimental results confirm that our proposed method is competitive and outperforms the state of the art BM3D in all considered subjective and objective quality measurements, particularly in preserving edges, textures and image contrast

Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregation

We introduce caption-guided face recognition (CGFR) as a new framework to improve the performance of commercial-off-the-shelf (COTS) face recognition (FR) systems. In contrast to combining soft biometrics (eg., facial marks, gender, and age) with face images, in this work, we use facial descriptions provided by face examiners as a piece of auxiliary information. However, due to the heterogeneity of the modalities, improving the performance by directly fusing the textual and facial features is very challenging, as both lie in different embedding spaces. In this paper, we propose a contextual feature aggregation module (CFAM) that addresses this issue by effectively exploiting the fine-grained word-region interaction and global image-caption association. Specifically, CFAM adopts a self-attention and a cross-attention scheme for improving the intra-modality and inter-modality relationship between the image and textual features, respectively. Additionally, we design a textual feature refinement module (TFRM) that refines the textual features of the pre-trained BERT encoder by updating the contextual embeddings. This module enhances the discriminative power of textual features with a cross-modal projection loss and realigns the word and caption embeddings with visual features by incorporating a visual-semantic alignment loss. We implemented the proposed CGFR framework on two face recognition models (ArcFace and AdaFace) and evaluated its performance on the Multi-Modal CelebA-HQ dataset. Our framework significantly improves the performance of ArcFace in both 1:1 verification and 1:N identification protocol.Comment: This article has been accepted for publication in the IEEE International Joint Conference on Biometrics (IJCB), 202

ROLE OF TECHNOLOGY TO TEACH SPEAKING SKILLS IN HSC LEVEL STUDENTS IN BANGLADESH

The study presents the way of teaching speaking skill in the context of English as a foreign language (EFL) with the best use of technology for the intermediate level students in Bangladesh. It also suggests the teachers regarding the ways of teaching spoken English to the Bangladeshi EFL learners through technology. The effectiveness and problems of using technology have also been depicted here. 20 teachers of six renowned colleges of Dhaka are interviewed as well as 4 questions are asked to the students about the inability of spoken English. After the survey it can be said undeniably that using technology in teaching spoken English to the intermediate students is more effective than any other means. At present, most of the students are using mobile phone, computer, laptop etc. and they are passing huge time. When they will get the opportunity of learning English, they will concentrate on it very easily. Most of the teachers and students are agreed to use technology for learning spoken English. Very few teachers are disagreed about it reason is that  they are not trained of using technology as well as they told if they are trained to us technology, they are eager to use computer, laptop for teaching.Keywords: Technology, teaching tactics, Projector, Spoken English,  Facebook

Community Participation-Based Environmental and Health Management in Dhaka City Suburban Slum Areas

The purpose of this article is to identify the role of the community in dealing with the problem of slum housing in the city of Dhaka with several approaches, namely collaboration between organizations and the role of the community itself. Several programs that have been implemented to solve this problem are public housing, slum reconstruction program, and Approach to Basteess. The potential of the impoverished to improve their living conditions and hence their productivity and income is demonstrated by the housing projects implemented by non-governmental organizations. Living under these conditions can lead to a variety of negative consequences, including decreased health, increased pollution, the spread of illness, and even criminal conduct. It is considered a disease in the city to have slums. It is considered a disease in the city to have slums. Build a residential environment that is conducive to various aspects of human progress in order to quickly achieve poverty reduction via comprehensive human development

Forecasting Temperature in the Coastal Area of Bay of Bengal-An Application of Box-Jenkins Seasonal ARIMA Model

Temperature is one of the most vital elements of the climate system and forecasting of the temperature helps the stakeholders those who are depends on it directly or indirectly to prepare  in advance. Country like Bangladesh whose economy mostly geared up by the agricultural product need to know the upcoming pattern of temperature beforehand to take necessary actions. This study has been conducted on the monthly maximum and minimum temperature data (1949-2012) from the second largest and port city of Bangladesh, Chittagong. Non-parametric Mann-Kendall test has been adopted to identify the trend of the series and found that though the maximum temperature is increasing but not significantly but the minimum temperature is increasing significantly. The anomaly plot is just portrait the ups and downs of minimum and maximum temperature and found minimum temperature is increasing from last two decades whereas the maximum temperature has abrupt changes with increase and decrease. The linear trend analysis shows the climate line for maximum and minimum temperature are 35.67 and 10.23 degree Celsius respectively and the rate for significant increase of minimum temperature is 0.07 degree Celsius. The forecasting Seasonal ARIMA model for maximum temperature is SARIMA (1, 1, 1) (2, 0, 0) [12] and for minimum temperature is SARIMA (1, 1, 1) (1, 0, 1) [12]. The resulted outcomes indicate the increasing pattern of temperature in upcoming days in this area of Bangladesh. Keywords: temperature, Seasonal ARIMA, forecasting, climate, Chittagon

Text-Guided Face Recognition using Multi-Granularity Cross-Modal Contrastive Learning

State-of-the-art face recognition (FR) models often experience a significant performance drop when dealing with facial images in surveillance scenarios where images are in low quality and often corrupted with noise. Leveraging facial characteristics, such as freckles, scars, gender, and ethnicity, becomes highly beneficial in improving FR performance in such scenarios. In this paper, we introduce text-guided face recognition (TGFR) to analyze the impact of integrating facial attributes in the form of natural language descriptions. We hypothesize that adding semantic information into the loop can significantly improve the image understanding capability of an FR algorithm compared to other soft biometrics. However, learning a discriminative joint embedding within the multimodal space poses a considerable challenge due to the semantic gap in the unaligned image-text representations, along with the complexities arising from ambiguous and incoherent textual descriptions of the face. To address these challenges, we introduce a face-caption alignment module (FCAM), which incorporates cross-modal contrastive losses across multiple granularities to maximize the mutual information between local and global features of the face-caption pair. Within FCAM, we refine both facial and textual features for learning aligned and discriminative features. We also design a face-caption fusion module (FCFM) that applies fine-grained interactions and coarse-grained associations among cross-modal features. Through extensive experiments conducted on three face-caption datasets, proposed TGFR demonstrates remarkable improvements, particularly on low-quality images, over existing FR models and outperforms other related methods and benchmarks.Comment: Accepted at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 202

Boron Nitride nanotube reinforced Titanium matrix composite

Boron nitride nanotube reinforcement at titanium matrix composite increased the strength of the composite both at room and high temperature. At higher sintering temperature, nanotube reacts with titanium first forming TiB2 transition phase at the interface and then in-situ formed TiB phases in the matrix, which is also responsible for enhanced mechanical properties