Mobile application based diagnosis has become an aid nowadays. For better diagnosis, the quality of image needs to be good. Automatic assessment of images will help the ophthalmologists to focus more on the diagnosis....
详细信息
images acquired in hazy conditions have degradations induced in them. Dehazing such images is a vexed and ill-posed problem. Scores of prior-based and learning-based approaches have been proposed to mitigate the effec...
详细信息
ISBN:
(数字)9783031581816
ISBN:
(纸本)9783031581809;9783031581816
images acquired in hazy conditions have degradations induced in them. Dehazing such images is a vexed and ill-posed problem. Scores of prior-based and learning-based approaches have been proposed to mitigate the effect of haze and generate haze-free images. Many conventional methods are constrained by their lack of awareness regarding scene depth and their incapacity to capture long-range dependencies. In this paper, a method that uses residual learning and vision transformers in an attention module is proposed. It essentially comprises two networks: In the first one, the network takes the ratio of a hazy image and the approximated transmission matrix to estimate a residual map. the second network takes this residual image as input and passes it through convolution layers before superposing it on the generated feature maps. It is then passed through global context and depth-aware transformer encoders to obtain channel attention. the attention module then infers the spatial attention map before generating the final haze-free image. Experimental results including several quantitative metrics demonstrate the efficiency and scalability of the suggested methodology.
Plant segmentation is an important application of computervisionprocessing technology in agriculture. Relying on plant segmentation technology, many crop problems can be significantly discovered and prevented. At th...
详细信息
Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are cr...
详细信息
ISBN:
(数字)9783031581816
ISBN:
(纸本)9783031581809;9783031581816
Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and treatment planning. Grad-CAM is a baseline that highlights the most critical regions of an image used in a deep learning model's decision-making process, increasing interpretability and trust in the results. It is applied in many computervision (CV) tasks such as classification and explanation. this study explores the principles of Explainable Deep Learning and its relevance to medical imaging, discusses various explainability techniques and their limitations, and examines medical imaging applications of Grad-CAM. the findings highlight the potential of Explainable Deep Learning and Grad-CAM in improving the accuracy and interpretability of deep learning models in medical imaging. the code is available in (https://***/ beasthunter758/GradEML).
Recent approaches to image captioning typically follow an encoder-decoder architecture. the feature vectors extracted from the region proposals obtained from an object detector network serve as input to encoder. Witho...
详细信息
ISBN:
(纸本)9783031581809;9783031581816
Recent approaches to image captioning typically follow an encoder-decoder architecture. the feature vectors extracted from the region proposals obtained from an object detector network serve as input to encoder. Without any explicit spatial information about the visual regions, the caption synthesis model is limited to learn relationship from captions only. However, the structure between the semantic units in images and sentences is different. this work introduces a grid based spatial position encoding scheme to learn relationship from both domains. Furthermore, bi-linear pooling is used with attention for exploiting spatial and channel-wise attention distribution to capture second order interaction between multi-modal inputs. these are integrated within the Transformer architecture achieving a competitive CIDEr score.
Human interaction recognition can be used in video surveillance to recognise human behaviour. the goal of this research is to classify human interaction by converting video snippets into dynamic images and deep CNN ar...
详细信息
Near-Infrared (NIR) images are widely used in a variety of low-light situations for security and safety applications. A colorised version of NIR images provide better image understanding and interpretation of features...
详细信息
ISBN:
(数字)9783031581816
ISBN:
(纸本)9783031581809;9783031581816
Near-Infrared (NIR) images are widely used in a variety of low-light situations for security and safety applications. A colorised version of NIR images provide better image understanding and interpretation of features. Because the number of NIR-RGB paired datasets is limited and often unavailable, a method to convert a given NIR image to an RGB image is highly desirable. the present work proposes an unsupervised image to image translation technique for generating colorized images (UGCI) for transforming an input NIR image to an RGB image. UGCI outperforms present NIR-RGB colorizing models and have shown approximately 57% improvement in terms of Frechet inception distance (FID) with reduced training time and less memory usage. Finally, a thorough comparative study based on different datasets is carried out to confirm superiority over leading colorization approaches in qualitative and quantitative assessments.
Color image demosaicking is key in developing low-cost digital cameras using a color filter array(CFA). Similarly, multispectral image demosaicking can be used to develop low-cost and portable multispectral cameras us...
详细信息
In this paper, we develop a static global scheduling scheme for mapping computervision and image procesaing (cvip) operations on distributed-memory multiprocessors. the scheduler operates on task graphs containing li...
详细信息
imageprocessing is often considered a good candidate for the application of parallel processing because of the large volumes of data and the complex algorithms commonly encountered. this paper presents a tutorial int...
详细信息
imageprocessing is often considered a good candidate for the application of parallel processing because of the large volumes of data and the complex algorithms commonly encountered. this paper presents a tutorial introduction to the field of parallel imageprocessing. After introducing the classes of parallel processing a brief review of architectures for parallel imageprocessing is presented. Software design for low-level imageprocessing and parallelism in high-level imageprocessing are discussed and an application of parallel processing to handwritten postcode recognition is described. the paper concludes with a look at future technology and market trends.
暂无评论