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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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Efficient Adaptive Weighted Minimization for Compressed Sensing Magnetic Resonance image Reconstruction 16

Efficient Adaptive Weighted Minimization for Compressed Sens...

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10th Indian conference on computer vision, Graphics and image processing (ICVGIP)

作者： Datta, Sumit Deka, Bhabesh Tezpur Univ Dept ECE CVIP Lab Tezpur 784028 Assam India

ISBN: (纸本)9781450347532

Compressed sensing magnetic resonance imaging (CSMRI) have demonstrated that it is possible to accelerate MRI scan time by reducing the number of measurements in the k-space without significant loss of anatomical details. The number of k-space measurements is roughly proportional to the sparsity of the MR signal under consideration. Recently, a few works on CSMRI have revealed that the sparsity of the MR signal can be enhanced by suitable weighting of different regularization priors. In this paper, we have proposed an efficient adaptive weighted reconstruction algorithm for the enhancement of sparsity of the MR image. Experimental results show that the proposed algorithm gives better reconstructions with less number of measurements without significant increase of the computational time compared to existing algorithms in this line.

关键词： Magnetic resonance imaging compressed sensing l(1)-norm total variation regularization image reconstruction

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High Capacity and Reversible Steganographic Technique with Authentication Capability 8th

High Capacity and Reversible Steganographic Technique with A...

引用

8th international conference on computer vision and image processing (cvip)

作者： Ansari, Iffat Rehman Hurrah, Nasir N. Khan, Mohd Ayyub Khan, Ekram AMU Zakir Husain Coll Engn & Technol Dept Elect Engn Aligarh Uttar Pradesh India AMU Univ Polytech Elect Engn Sect Aligarh Uttar Pradesh India

ISBN: (纸本)9783031585340;9783031585357

This paper proposes a high capacity and reversible DCT-based steganographic technique with authentication capability for 8-bit grayscale general and medical images. The technique obtains 64 DCT coefficients by performing DCT on an (8 x 8) cover image block and only uses selected AC coefficients to hide the secret message. The proposed scheme embeds a fragile watermark using LSB substitution in the selected pixel of the (8 x 8) stego image block to verify the stego image's authenticity. The proposed scheme's performance is evaluated based on payload, imperceptibility, fragility, and image authentication analyses. Simulation results confirm that the steganographic technique is reversible, supports a good payload, and produces high quality watermarked images. The proposed technique outperforms state-of-the-art techniques in embedding capacity while maintaining good image quality. The fragility and authentication analysis confirm that our proposed scheme is fragile in nature and can detect various image processing and geometrical attacks. Our scheme can verify the stego image's authenticity and is also capable of detecting whether the message has been corrupted.

关键词： Steganography Fragile Watermark DCT Coefficients Embedding Capacity image Authentication

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Feature Fusion and Multi-head Attention Based Hindi Captioner 8th

Feature Fusion and Multi-head Attention Based Hindi Captione...

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8th international conference on computer vision and image processing (cvip)

作者： Meghwal, Virendra Kumar Mittal, Namita Singh, Girdhari Malaviya Natl Inst Technol Jaipur Rajasthan India

ISBN: (纸本)9783031581809;9783031581816

Deep learning-based methods are extensively used in image captioning, but most of these methods depend on features from a single encoder for generating captions. Different encoders capture different features of an image, and thus, using features from multiple encoders may help improve the models' performance. Moreover, there needs to be more research on Hindi caption generation on large datasets such asMSCOCO. Recently, transformers have performed well on tasks such as image classification and object detection. One such transformer is the Swin Transformer. It captures both local as well as global information present in the image. A Faster RCNN, on the other hand, captures only local (object-level) information but does not capture global details. Using a single image feature generation method might sometimes result in incorrect feature generation, or some important objects may be missed while generating the feature vector. This problem can be mitigated by combining features from different methods. Furthermore, as local features-based models have produced better results in different domains, utilizing both Swin Transformer and Faster RCNN may result in better captioning models. This work proposes to use Swin Transformer-based image features along with Faster RCNN-based image features to generate Hindi captions for images. A decoder with two GRUs and Multi-head Attention uses these image features to build Hindi captions. Experiments demonstrate that the proposed method can generate high-quality captions while improving the performance of automatic evaluation metrics, establishing the method's efficacy.

关键词： image Captioning Feature Fusion computer vision Natural Language processing

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A Bag of Constrained Visual Words Model for image Representation 3rd

A Bag of Constrained Visual Words Model for Image Representa...

引用

3rd international conference on computer vision and image processing (cvip)

作者： Mukherjee, Anindita Sil, Jaya Chowdhury, Ananda S. Dream Inst Technol Kolkata India IIEST Sibpur Howrah India Jadavpur Univ Kolkata 700032 India

ISBN: (纸本)9789813292918;9789813292901

We propose a bag of constrained visual words model for image representation. Each image under this model is considered to be an aggregation of patches. SURF features are used to describe each patch. Two sets of constraints, namely, the must-link and the cannot-link, are developed for each patch in a completely unsupervised manner. The constraints are formulated using the distance information among different patches as well as statistical analysis of the entire patch data. All the patches from the image set under consideration are then quantized using the Linear-time-Constrained Vector Quantization Error (LCVQE), a fast yet accurate constrained k-means algorithm. The resulting clusters, which we term as constrained visual words, are then used to label the patches in the images. In this way, we model an image as a bag (histogram) of constrained visual words and then show its utility for image retrieval. Clustering as well as initial retrieval results on COIL-100 dataset indicate the merit of our approach.

关键词： image representation Constrained visual words LCVQE image retrieval

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Deep Learning Framework for Detection of an Illicit Drug Abuser Using Facial image 3rd

Deep Learning Framework for Detection of an Illicit Drug Abu...

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3rd international conference on computer vision and image processing (cvip)

作者： Gupta, Tanu Goyal, Meghna Kumar, Goutam Raman, Balasubramanian Indian Inst Technol Roorkee Roorkee Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

The detection of an illicit drug abuser by analyzing the subject's facial image has been an active topic in the field of machine learning research. The big question here is up to what extent and with what accuracy can a computer model help us to identify if a person is an illicit drug abuser only by analyzing the subject's facial image. The main objective of this paper is to propose a framework which can identify an illicit drug abuser just by giving an image of the subject as an input. The paper proposes a framework which relies on Deep Convolutional Neural Network (DCNN) in combination with Support Vector Machine (SVM) classifier for detecting an illicit drug abuser's face. We have created dataset consisting of 221 illicit drug abusers' facial images which present various expressions, aging effects, and orientations. We have taken random 221 non-abusers' facial images from available dataset named as, Labeled Faces in the Wild (LFW). The experiments are performed using both datasets to attain the objective. The proposed model can predict if the person in an image is an illicit drug abuser or not with an accuracy of 98.5%. The final results show the importance of the proposed model by comparing the accuracies obtained in the experiments performed.

关键词： Deep learning Convolutional neural network Face recognition

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MCCNet: A Multi-scale Cross Connection Network for Low-Light image Enhancement 8th

MCCNet: A Multi-scale Cross Connection Network for Low-Light...

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8th international conference on computer vision and image processing (cvip)

作者： Panda, Santosh Kumar Nayak, Devidutta Sa, Pankaj Kumar Natl Inst Technol Rourkela Rourkela Odisha India

ISBN: (纸本)9783031581809;9783031581816

Low-light images are captured either during nighttime or in environments with limited illumination. These images typically suffer from reduced visibility and increased noise levels, making it challenging to extract relevant information. Enhancing such images is a decisive task in many fields, like medical imaging and night surveillance, where the clarity and detailing of the image are required for better image processing. This article introduces a novel deep-learning-based approach for enhancing low-light images. Our technique utilizes a multi-scale cross-connection network (MCCNet) that employs region-specific analysis to improve feature extraction. Moreover, our method also includes a calibrated network to fine-tune the features to get improved results. The critical section of the model is the region selection of the images, where some overlapping regions help provide contextual information between the extracted features. The cross-connection network helps to share the distinct features extracted and concatenate them to a single feature block, further improving the model's performance. Substantial simulations show our model's haughty performance compared with the state-of-the-art benchmark methods. The GitHub code is available at https:// ***/santoshpanda1995/Multiscale- cross- connection-network

关键词： Low-light image enhancement (LLIE) Multi-scale Regions Cross-connection Calibrated-CNN

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Improved image Captioning Using GAN and ViT 8th

Improved Image Captioning Using GAN and ViT

引用

8th international conference on computer vision and image processing (cvip)

作者： Rao, Vrushank D. Shashank, B. N. Bhattu, S. Nagesh Natl Inst Technol Andhra Pradesh Dept Comp Sci & Engn Tadepalligudem India

ISBN: (纸本)9783031585340;9783031585357

Encoder-decoder architectures are widely used in solving image captioning applications. Convolutional encoders and recurrent decoders are prominently used for such applications. Recent advances in transformer-based designs have made SOTA performances in solving various language and vision tasks. This work inspects the research question of using transformer-based encoder and decoder in building an effective pipeline for image captioning. An adversarial objective using a Generative Adversarial Network is used to improve the diversity of the captions generated. The generator component of our model utilizes a ViT encoder and a transformer decoder to generate semantically meaningful captions for a given image. To enhance the quality and authenticity of the generated captions, we introduce a discriminator component built using a transformer decoder. The discriminator evaluates the captions by considering both the image and the caption generated by the generator. By training this architecture, we aim to ensure that the generator produces captions that are indistinguishable from real captions, increasing the overall quality of the generated outputs. Through extensive experimentation, we demonstrate the effectiveness of our approach in generating diverse and contextually appropriate captions for various images. We evaluate our model on benchmark datasets and compare its performance against existing state-of-the-art image captioning methods. The proposed approach has achieved superior results compared to previous methods, as demonstrated by improved caption accuracy metrics such as BLEU-3, BLEU-4, and other relevant accuracy measures.

关键词： vision Transformers Data2Vec image Captioning

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Indoor-Outdoor Scene Classification with Residual Convolutional Neural Network 3rd

Indoor-Outdoor Scene Classification with Residual Convolutio...

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3rd international conference on computer vision and image processing (cvip)

作者： Kumari, Seema Jha, Ranjeet Ranjan Bhavsar, Arnav Nigam, Aditya IIT Mandi MAS Lab Sch Comp & Elect Engn Kamand India

ISBN: (纸本)9789813292918;9789813292901

In this paper, we demonstrate the effectiveness of a customized ResNet to address the problem of indoor-outdoor scene classification both for color images as well as depth images. Such an approach can serve as an initial step in a scene classification/retrieval pipeline or a single-image depth estimation task. The classification framework is developed based on Residual Convolutional Neural Network (ResNet-18) to classify any random scene as indoor or outdoor. We also demonstrate the invariance of the classification performance with respect to different weather conditions of outdoor scenes (which one can commonly encounter). The performance of our classification strategy is analyzed on different varieties of publicly available datasets of indoor and outdoor scenes that also have corresponding depth maps. The suggested approach achieves almost an ideal performance in many scenarios, for both color and depth images, across datasets. We also show positive comparisons with other state-of-the-art methods.

关键词： Scene classification RCNN Scene analysis Depth information image retrieval

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A Nonlinear Modified CONVEF-AD Based Approach for Low-Dose Sinogram Restoration

A Nonlinear Modified CONVEF-AD Based Approach for Low-Dose S...

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1st international conference on computer vision and image processing (cvip)

作者： Tiwari, Shailendra Srivastava, Rajeev Arya, K. V. Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India ABV Indian Inst Informat Technol & Management Multimedia & Informat Secur Lab Gwalior 474015 India

ISBN: (纸本)9789811021046;9789811021039

Preprocessing the noisy sinogram before reconstruction is an effective and efficient way to solve the low-dose X-ray computed tomography (CT) problem. The objective of this paper is to develop a low-dose CT image reconstruction method based on statistical sonogram smoothing approach. The proposed method is casted into a variational framework and the solution of the method is based on minimization of energy functional. The solution of the method consists of two terms, viz., data fidelity term and a regularization term. The data fidelity term is obtained by minimizing the negative log likelihood of the signal-dependent Gaussian probability distribution which depicts the noise distribution in low-dose X-ray CT. The second term, i.e., regularization term is a nonlinear CONvolutional Virtual Electric Field Anisotropic Diffusion (CONVEF-AD) based filter which is an extension of Perona-Malik (P-M) anisotropic diffusion filter. The main task of regularization function is to address the issue of ill-posedness of the solution of the first term. The proposed method is capable of dealing with both signal-dependent and signal-independent Gaussian noise, i.e., mixed noise. For experimentation purpose, two different sinograms generated from test phantom images are used. The performance of the proposed method is compared with that of existing methods. The obtained results show that the proposed method outperforms many recent approaches and is capable of removing the mixed noise in low-dose X-ray CT.

关键词： X-ray computed tomography Statistical sinogram smoothing image reconstruction algorithm Noise reduction Anisotropic diffusion

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Copy-Move image Forgery Detection Using Gray-Tones with Texture Description 3rd

Copy-Move Image Forgery Detection Using Gray-Tones with Text...

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3rd international conference on computer vision and image processing (cvip)

作者： Dixit, Anuja Bag, Soumen Indian Sch Mines Dept Comp Sci & Engn Indian Inst Technol Dhanbad Bihar India

ISBN: (纸本)9789813292918;9789813292901

Copy-move forgery is a well-known image forgery technique. In this image manipulation method, a certain area of the image is replicated and affixed over the same image on different locations. Most of the times replicated segments suffer from multiple post-processing and geometrical attacks to hide sign of tampering. We have used block-based method for forgery detection. In block-based proficiencies, image is parted into partially overlapping blocks. Features are extracted corresponding to blocks. In the proposed scheme, we have computed Gray-Level Co-occurrence Matrix (GLCM) for blocks. Singular Value Decomposition (SVD) is applied over GLCM to find singular values. We have calculated Local Binary Pattern (LBP) for all blocks. The singular values and LBP features combinedly construct feature vector corresponding to blocks. These feature vectors are sorted lexicographically. Further, similar blocks discovered to identify replicated section of image. To ensure endurance of the proposed methods, Detection Accuracy (DA), False Positive Rate (FPR), and F-Measure are calculated and compared with existing methods. Experimental results establish the validity of proposed scheme for precise detection, even when meddled region of image sustain distortion due to brightness change, blurring, color reduction, and contrast adjustment.

关键词： Copy-move image forgery Feature extraction image forensics Local binary pattern Singular value decomposition

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