Even though forgery detection is a well matured topic, reporting on the same for detection of forgery in night time outdoor scenes is not explored to date. Despite the lack of appropriate benchmark video datasets for forgery detection at night time using infrared (IR) video modality, we designed a novel ground truth annotated forged video dataset named as “NV2CIR (Night Vision Video Forensic Challenges based IR Forged Dataset)” in real-world night time situations. The dataset contains various 310 infrared imaging based forged videos (i.e., object based forgery, interframe forgery, and intraframe forgery) and their corresponding 310 authentic videos. The article also proposed a novel framework named as “AFOD-Net (attention guided fake object detection network)” for localization of spliced objects in night vision outdoor scenarios. The proposed network employs a long short term memory (LSTM) module and our proposed spliced object attention module (SOA) so as to precisely localize the spatio-temporal spliced regions. Experimental results show that AFOD-Net significantly improves the performance for localizing the spliced objects on our designed spliced video dataset with mean average precision (mAP) of 85.16%.

Due to the unavailability of sufficient amounts of breast histopathological images for medical abnormality detection especially for many rare cancer stages, the applicability of the traditional deep learning models to achieve good prediction performance is a challenging task. To address such challenges, we proposed Meta Attention Guided Few-Shot Learning (MAG-FSL) for a robust and highly effective model for Domain Adaptive Few-Shot Learning (DA-FSL) problem in breast abnormality classification using histopathological images. MAG-FSL is investigated on two publicly accessible breast cancer databases i.e., BreakHis and BreastCancer-IDC-Grades. Experimental results reveal that our proposed MAG-FSL significantly outperformed the state-of-the-art Few-Shot Learning (FSL) and DA-FSL methods for breast histopathological image classification in the single domain and the shifted domain FSL problems. For the single domain FSL problem, we achieved an average accuracy of 92.42%. For the DA-FSL problem, we achieved average accuracies of 76.33% and 69.95% on BreakHis and BreastCancer-IDC-Grades databases, respectively.

The accurate prediction of cancer from microscopic biopsy images has always been a major challenge for medical practitioners and pathologists who manually observe the shape and structure of the cells from tissues under a microscope. Mathematical modelling of cell proliferation helps to predict tumour sizes and optimizes the treatment procedure. This paper introduces a cell growth estimation function that uncovers the growth behaviour of benign and malignant cells. To analyse the cellular level information from tissue images, we propose a minimized cellular graph (MCG) development method. The method extracts cells and produces different features that are useful in classifying benign and malignant tissues. The method’s graphical features enable a precise and timely exploration of huge amounts of data and can help in making predictions and informed decisions. This paper introduces an algorithm for constructing a minimized cellular graph which reduces the computational complexity. A comparative study is performed based on the state‐of‐the‐art classifiers, SVM, decision tree, random forest, nearest neighbor, LDA, Naive Bayes, and ANN. The experimental data are obtained from the BreakHis dataset, which contains 2480 benign and 5429 malignant histopathological images. The proposed technique achieves a 97.7% classification accuracy which is 7% higher than that of the other graph feature‐based classification methods. A comparative study reveals a performance improvement for breast cancer classification compared to the state‐of‐the‐art techniques.

Poor visibility due to existence of fog and other associated particles in the atmosphere is the most fundamental problem for current vision applications. Recently, techniques for visibility enhancement of images have received a significant attention. However, validation of the existing techniques remains scare due to the lack of balanced distribution on the existing datasets. In this paper, a newly designed dataset entitled “SAMEER-TU Outdoor Dataset” is proposed. The dataset contains 5880 images of urban scenes in fog, poor illumination and clear conditions. Also, ground truths are provided in terms of meteorological weather parameters and corresponding clear scene images of the degraded images. On the designed dataset, quantitative analysis of existing visibility enhancement techniques (i. e., conventional and deep learning techniques) are performed based on qualitative evaluation metrics. It comes as no surprise that the existing visibility enhancement techniques and there still existing significant for further improvement.

Detection of moving objects in outdoor environments is an extremely researched topic. However, studies on moving object detection in complex atmospheric/weather conditions are limited, primarily because of the absence of any relevant benchmark dataset. To address this disparity, we introduce a novel benchmark video dataset entitled “Extended Tripura University Video Dataset (E-TUVD)” which is a diverse dataset of complex atmospheric/weather conditions. Currently, E-TUVD is the largest video dataset for moving object detection under degraded atmospheric/weather conditions. The dataset comprises 147 video clips spanning 1-5 minutes in duration of each video clips. Because of the requirement of evaluating any object detection model, this study emphasizes on generation of ground-truth images of salient moving objects on E-TUVD. Using this dataset, we assessed the performance of several state-of-the-art algorithms, considering both the ability to detect moving objects and visibility enhancement under such complex conditions. The method with the best performance was used to investigate the effectiveness of visibility enhancement of atmospheric/weather degraded image sequences for accurate moving object detection. Results and analysis reveal that effective enhancement can significantly improve the ability of detection algorithms under degraded atmospheric/weather conditions to resemble the true properties of moving objects in terms of pixel oriented binary masks.

Hand held gun detection has an important application in both the field of video forensic and surveillance, because, gun is operative by hand only while committing any crime with it. The significant application encompasses the vulnerable places, such as around airport, marketplace, shopping malls, etc. In view of non‐availability of relevant public data set, this study provides a newly created mimicked video data set for detection of gun carried by a person and entitled as Tripura University Video Data set for Crime‐Scene‐Analysis (TUVD‐CSA). Effects of illumination, occlusion, rotation, pan, tilt, scaling of gun are effectively demonstrated in it. Moreover, the authors proposed an Iterative Model Generation Framework (IMGF) for gun detection, which is immune to scaling and rotation. Instead of locating the best matched object (gun) in the whole reference image to a query model via exhaustive search, IMGF searches only where the moving person carrying gun appears, which drastically reduces the computational overhead associated with a general template matching scheme. This has been employed by the background subtraction algorithm. Experimental results demonstrate that the proposed IMGF performs efficiently in gun detection with lesser number of true‐negatives compared with the state‐of‐the‐art methods.

Foreground segmentation of moving objects in adverse atmospheric conditions such as fog, rain, low light, and dust is a challenging task in computer vision. The advantages of thermal infrared imaging at night time under adverse atmospheric conditions have been demonstrated, which are due to the long wavelength. However, the existing state-of-the-art object detection techniques have not been useful in such scenarios. In this paper, we propose an improved background model that utilizes both thermal pixel intensity features and spatial video salient features. The proposed spatial video salient features are represented as an Akin-based per-pixel Boolean string over a local region block, and depend on the effect of neighboring pixels on a center pixel. The result of this Boolean procedure is referred to as the- Akin-Based Local Whitening Boolean Pattern (ALWBP), which differentiates foreground and background region accurately, even against a cluttered background. The background model is controlled via 1) the automatic adaptation of parameters such as the decision threshold $\text{R}_{\mathrm {T}}$ and, learning parameter L, and 2) the updating of background samples $\text{B}_{\mathrm {sample\_{}int}}$ and,- $\text{B}_{\mathrm {sample\_{}ALWBP}}$ to minimize 1) the effect of the background dynamics of outdoor scenes and 2) the temperature polarity changes during the maiden appearance of a moving object in thermal frame sequences. The performance of this model is evaluated using nine existing standard segmentation performance metrics on our newly created-Tripura University Video Dataset at Night Time (TU-VDN) and on the publicly available CDnet-2014 dataset. Our newly created weather-degraded video dataset, namely, TU-VDN, consists of sixty video sequences that represent four atmospheric conditions, namely, low light, dust, rain, and fog. The results of a performance comparison with 14 state-of-the-art detection techniques also demonstrate the high accuracy of the proposed technique.

Rheumatologists determine treatment plan based on the inflammation of knee joints affected by arthritis. Extraction of the inflamed region or hotspot from the knee thermogram is the prerequisite for grading of inflammation and classification of different arthritis. In this paper, we propose an automatic method for extracting the inflamed region from the knee thermograms. We propose an ensemble technique to arrive at a consensus segmentation of the hotspot region. We have used variation of information based information theoretic approach to generate consensus segmentation. The fusion of multiple segmentation maps is achieved using local search based greedy iterated conditional modes algorithm to obtain final segmentation result. Experiments show that our proposal scores significantly better in detecting hotspots in more than 50 inflammatory knee thermograms.

Even though thermal infrared images captured during night time are available in some publicly available datasets, such images acquisitioned in adverse weather conditions such as low light, dust, rain, fog etc. are not reported as yet to the best of our knowledge. Because of these deficiencies, object detection techniques applicable in weather affected night thermal infrared images have a very limited reporting in literature. In the present scope, we discussed the acquisition, creation, design, and ground truth annotation of a new video dataset consisting of nearly 60 videos representing 4 atmospheric conditions: low light, dust, rain, fog, named as Tripura University Video Dataset at Night time (TU-VDN) in adverse weather conditions, suitable for this purpose. The objective is to provide a night video dataset containing moving objects with annotated ground truth in the image frame sequences. Using TU-VDN a comparative study is made between the results of ten existing state-of-the-art moving object segmentation methods.

Segmentation of suspicious regions (SRs) of a thermal breast image (TBI) is a very significant and challenging problem for the identification of breast cancer. Therefore, in this work, we have proposed an active contour model for the segmentation of the SRs in TBI. The proposed segmentation method combines three significant steps. First, a novel method, called smaller-peaks corresponding to the high-intensity-pixels and the centroid-knowledge of SRs (SCH-CS), is proposed to approximately locate the SRs, whose contours are later used as the initial evolving curves of the level set method (LSM). Second, a new energy functional, called different local priorities embedded (DLPE), is proposed regarding the level set function. DLPE is then minimized using the interleaved level set evolution to segment the potential SRs in TBI more accurately. Finally, a new stopping criterion is incorporated into the proposed LSM. The proposed LSM not only increases the segmentation speed but also ameliorates the segmentation accuracy. The performance of our SR segmentation method was evaluated on two TBI databases, namely, DMR-IR and DBT-TU-JU, and the average segmentation accuracies obtained on these databases are 72.18% and 71.26% respectively, which are better than the other state-of-the-art methods. Beside this, a novel framework to analyze TBIs is proposed for differentiating abnormal and normal breasts on the basis of the segmented SRs. We have also shown experimentally that investigating only the SRs instead of the whole breast is more effective in differentiating abnormal and normal breasts.

In this paper, we present an approach towards object detection and recognition from various environmental conditions such as foggy morning, dust scenarios, and night vision. The goal of the approach is to develop a holistic feature extraction method over object image patch. To categorize objects, the experimental evaluation has prepared through four classifiers. Investigational results with our own collected video sequences are reported to demonstrate the accuracy of the proposed approach.

Moving object detection has been extensively studied during the last few decades. However the detection of moving objects in different degraded atmospheric conditions (i.e. fog, haze, dust and poor illumination) is less understood. This is possibly because of the lack of a suitable and publically-available video dataset under such weather conditions within which salient objects are unambiguously defined and annotated. This paper describes the creation and design of a new video dataset named as “Tripura University Video dataset (TUVD)” which specifically addresses degraded atmospheric weather conditions for moving object detection in outdoor scenes. The objective is to provide video dataset containing moving objects with annotated ground truth in the form of images of the salient objects in the image sequences. Currently, TUVD contains 55 videos of moving objects (vehicles, animals and pedestrian) under degraded atmospheric conditions. Using TUVD a comparison is made between the results of seven existing state-of-the-art visibility enhancement methods. Quantitative assessment of image quality is achieved using four no-reference image based quality assessment metrics. Overall, the most efficient method for visibility restoration of outdoor scenes is found to be one based on multi-scale fusion, although most of the other algorithms tested show interesting capability in specific cases.

The advancement of research in a specific area of clinical diagnosis crucially depends on the availability and quality of the radiology and other test related databases accompanied by ground truth and additional necessary medical findings. This paper describes the creation of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) breast thermogram database. The objective of creating the DBT-TU-JU database is to provide a breast thermogram database that is annotated with the ground-truth images of the suspicious regions. Along with the result of breast thermography, the database comprises of the results of other breast imaging methodologies. A standard breast thermogram acquisition protocol suite comprising of several critical factors has been designed for the collection of breast thermograms. Currently, the DBT-TU-JU database contains 1100 breast thermograms of 100 subjects. Due to the necessity of evaluating any breast abnormality detection system, this study emphasizes the generation of the ground-truth images of the hotspot areas, whose presence in a breast thermogram signifies the presence of breast abnormality. With the generated ground-truth images, we compared the results of six state-of-the-art image segmentation methods using five supervised evaluation metrics to identify the proficient segmentation methods for hotspot extraction. Based on the evaluation results, the fractional-order Darwinian particle swarm optimization, region growing, mean shift, and fuzzy c-means clustering are found to be more efficient in comparison to k-means clustering and threshold-based segmentation methods.

In this paper one investigation has been done to find the optimum level of fusion to find a fused image from visual as well as thermal images. Because of the use of face recognition system in critical areas like, authenticating an authorized person in highly secured areas, investigation of criminals, online monitoring etc, face recognition system should be very robust and accurate one. This work is an attempt to fuse visual and thermal face images at optimum level to extract the advantages of visual as well as thermal images. In our work, Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database has been used for the visual and thermal images. Among all the experiments a maximum recognition result obtained is 93%.