Optical pressure sensors based plantar image segmenting using an improved fully convolutional network

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery.

To facilitate the management of detonators, an automatic spotting system is proposed for detonator coded characters based on convolutional neural networks. The system contains a multi-scale detection network, a multi-label recognition network and a post-processing layer. An improved fully convolution network (FCN) is designed as the multi-scale detection network to obtain the accurate response map of the detonator image. Two subnetworks are parallel integrated into the FCN to perform a coarse-to-fine detection. An improved Jaccard loss function with a regularization term is defined to train the FCN. The region of interest (ROI) of the detonator image is achieved when the response map is post-processed by the post-processing layer. Finally, a modified multi-label network is used to recognize the detonator coded characters in the ROI. The experimental results indicate that the proposed system achieves a better spotting performance for detonator coded characters than the state-of-the-art text spotting methods in terms of accuracy and efficiency.

Existing segmentation and augmentation techniques on convolutional neural network (CNN) has produced remarkable progress in object detection. However, the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process, along with the context of the individual CNN algorithm. In this paper, we investigate the effect of a photometric variation like brightness and sharpness on different CNN. We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation. Our approach has been justified by the experimental result obtained from the PASCAL VOC 2007 dataset, with object detection CNN algorithms such as YOLOv3 (You Only Look Once), Faster R-CNN (Region-based CNN), and SSD (Single Shot Multibox Detector). Each CNN model shows performance loss for varying sharpness and brightness, ranging between −80% to 80%. It was further shown that compared to random augmentation, the augmented dataset with weak photometric changes delivered high performance, but the photometric augmentation range differs for each model. Concurrently, we discuss some research questions that benefit the direction of the study. The results prove the importance of adaptive augmentation for individual CNN model, subjecting towards the robustness of object detection.

Researchers are studying CNN (convolutional neural networks) in various ways for image classification. Sometimes, they must classify two or more objects in an image into different situations according to their location. We developed a new learning method that colored objects from images and extracted them to distinguish the relationship between objects with different colors. We can apply this method in certain situations, such as pedestrians in a crosswalk. This paper presents a method for learning pedestrian situations on CNN using Mask R-CNN (Region-based CNN) and CDA (Crosswalk Detection Algorithm). With this method, we classified the location of the pedestrians into two situations: safety and danger. We organized the process of preprocessing and learning images into three stages. In Stage 1, we used Mask R-CNN to detect pedestrians. In Stage 2, we detected crosswalks with the CDA and placed colors on detected objects. In Stage 3, we combined crosswalks and pedestrian objects into one image and then, learned the image to CNN. We trained ResNet50 and Xception using images in the proposed method and evaluated the accuracy of the results. When tested experimentally, ResNet50 exhibited 96.7% accuracy and Xception showed 98.7% accuracy. We then created an image that simplified the situation with two colored boxes of crosswalks and pedestrians. We confirmed that the learned CNN with the images of colored boxes could classify the same test images applied in the previous experiment with 96% accuracy by ResNet50. This result indicates that the proposed system is suitable for classifying pedestrian safety and dangerous situations by accurately dividing the positions of the two objects.

The current instance segmentation method can achieve satisfactory results in common scenarios. However, under the overlap or partial occlusion between targets caused by the complex scenes, accurate segmentation of pigs remains a challenging task. To address the problem, the authors propose an instance segmentation method based on Mask Scoring region‐based convolutional neural networks (R‐CNN) (MS R‐CNN), which creates the adversarial network called MaskDis in the head branch of MS R‐CNN. The MaskDis is trained as a discriminator using a generative adversarial network, and the MS R‐CNN model is used as a generator during model training. The adversarial training enables the generator to learn context information and features at the pixel level, which effectively improves the segmentation quality under pigs’ overlapping or dense occlusions scenes. Experimental conducted on the pig object segmentation dataset show that the proposed approach achieves a precision of 92.03%, a recall of 92.18%, and an F1 score of 0.9210. Compared with the basic MS R‐CNN model, the approach achieved a 2.25% improvement in precision and 1.18% improvement in F1 score. Furthermore, the improved approach outperformed advanced instance segmentation methods such as YOLACT, Swin Transformer, YOLOv5‐seg, and SOLOv2 on COCO evaluation metrics. These experimental results demonstrate the effectiveness of the proposed approach in instance segmentation of pigs in complex scenes, providing technical support for non‐contact pig automatic management.

Objective: Accurate and fast automatic segmentation of organs is a key step for efficient planning for radiation therapy. In this paper, we propose the Multi-mode CNNs, which is for accurate and fast automatic segmentation of mandible, parotid glands, brainstem, optic nerves, cerebellum, eyes, lens, pituitary, thyroid, temporal lobes, brain and head respectively in 3D CT image of head and neck.Methods: The proposed Multi-mode CNNs consist of three convolutional neural networks. The first CNN is a simple classification network to distinguish slices of head and neck. It accelerates the next step by reducing the search space. The second network is a 3DCNN for locating the region of interest (ROI) of organs. It can enhance the robustness of the algorithm to CT images from different hospitals. The third network is a full convolution network (FCN) based on U-Net. This is a pixel-wise detailed segmented network to classify voxels in a region of interest and generate segmentation results. We used public and clinical datasets to evaluate our algorithm. The public datasets are from the MICCAI 2015 Head and Neck Auto Segmentation Grand Challenge. For the private datasets, we collected 3D CT images of 88 patients from A and B Hospital.Results: For the public datasets, our segmentation results of the nine organs in the challenge surpassed the first rank. For the private datasets, clinical trials have proved that it is effective for real image data in different hospitals. The average total segmentation time for one patient with above whole organs is less than one minute, which indicates proposed method having clinical value.Conclusion: The proposed Multi-mode CNNs can be used to accurate and fast automatic segmentation of organs in radiation therapy. It can assist doctors in outlining the organs during the planning of radiation therapy in the clinic.

Hybrid CNN-transformer network for interactive learning of challenging musculoskeletal images

In this paper, a new convolution neural network method is proposed for the inspection and classification of galvanized stamping parts. Firstly, all workpieces are divided into normal and defective by image processing, and then the defective workpieces extracted from the region of interest (ROI) area are input to the trained fully convolutional networks (FCN). The network utilizes an end-to-end and pixel-to-pixel training convolution network that is currently the most advanced technology in semantic segmentation, predicts result of each pixel. Secondly, we mark the different pixel values of the workpiece, defect and background for the training image, and use the pixel value and the number of pixels to realize the recognition of the defects of the output picture. Finally, the defect area's threshold depended on the needs of the project is set to achieve the specific classification of the workpiece. The experiment results show that the proposed method can successfully achieve defect detection and classification of galvanized stamping parts under ordinary camera and illumination conditions, and its accuracy can reach 99.6%. Moreover, it overcomes the problem of complex image preprocessing and difficult feature extraction and performs better adaptability.

In biometric based authentication system, Iris is one of the most extensively used biometric trait as it has seen ground breaking research in both region of interest extraction and recognition. Several researchers in the field of biometric based authentication systems have claimed that the main reason for several matching errors is the poor segmentation of the trait. The task of segmentation for a biometric based authentication system is one of the most crucial, as most of the matching and recognition algorithms are performed on the particular region of interest in an acquired image. In this paper we propose two novel end to end convolutional neural network based architectures for region of interest extraction in an iris. The proposed architectures take the image of an eye as input and produce two circular regions of interests for the Iris and Pupil respectively. The two networks proposed were inspired from two state of the art object detection networks Faster RCNN (Region based Convolutional Neural Network) and SSD (Single Shot Multi-Box Detector), both these modified networks were trained for 8000 images. Experimental analysis performed proves that both of our techniques have very high performance in terms of accuracy's and overlaps.

In this paper we propose the Localized Quality Aware Image Denoising (LQAID) technique for image denoising using deep convolutional neural networks (CNNs). LQAID relies on local quality estimates over global cues like noise standard deviation since the perceptual quality of a noisy image is typically spatially varying. Specifically, we use localized quality maps generated using DistNet, a spatial quality map estimation method. These quality maps are used to augment the noisy image and guide the denoising process. The augmented noisy image is denoised using a deep fully convolutional network (FCN) trained using mean square error (MSE) as the loss function. The proposed approach shows state-of-the-art performance both qualitatively and quantitatively on two vision datasets: TID 2008 and BSD500. We also show that the proposed approach possesses excellent generalization ability. Lastly, the proposed approach is completely blind since it neither requires information about the strength of the additive noise nor does it try to explicitly estimate it.

ABSTRACTAircraft detection is one of hot issues of remote sensing (RS) image interpretation. Different from existing methods which aim at detecting objects in regions of interest (ROI) of airports, in this work, we propose a novel and effective aircraft detection framework based on convolutional neural networks (CNN) to detect multi-scale targets in extremely large and complicated scenes. In particular, we design a constrained EdgeBoxes (CEdgeBoxes) approach to generate a modest number of target candidates quickly and precisely. Then, in order to overcome the drawbacks of using handcrafted features and traditional classifiers, a modified GoogLeNet combined with Fast Region-based CNN (R-CNN) is designed to extract useful features from RS images and then detect various kinds of multi-scale aircrafts. Most importantly, we propose a new multi-model ensemble method to decrease the false alarm rate (FAR) caused by the imbalanced distribution of complicated targets and backgrounds. Thorough experiments, which are conducted on the dataset acquired by QuickBird with a resolution of 0.6 m, demonstrate our method can effectively detect aircrafts in large scenes with low FARs.

Early detection and treatment are crucial for recovery from cancer. Due to rapid advancement in medical imaging techniques, today doctors can scan various body parts from different perspectives to diagnose cancer or see the progress during a course of treatment. Such techniques are less painful and require less preparation and recovery. The recent evolution of AI, particularly deep learning algorithms have shown potential in computer-aided diagnosis of cancer. These include analysis of cancer images for detection of tumor region, prognosis, skin tissue inspection, etc. Convolutional Neural Network (CNN) based image processing techniques are showing promising results in the diagnosis of breast cancer, skin cancer, prostate cancer, lung cancer, etc. Literature shows many other CNN-based models such as feed-forward neural network, Deep CNN, Multi-Crop CNN, fully convolutional networks (FCNs), Deep Fully Convolutional Network (DFCNet), Recurrent neural networks, etc. The CNN focus solely on the image-specific features and hence require a lesser number of input parameters. A CNN model reduces the size of the input image vector without losing the features critical for making an accurate prediction. This makes it easier to process and reduces computational complexity. CNNs are also not sensitive to the object positioning in an image since the approximate position of image motifs relative to others is known. CNN deals with the variations in images through augmentation and other pre-processing techniques. This makes them robust to various straining conditions in images. This chapter presents an overview of a generic CNN architecture followed by a discussion on major advances in CNN models and their applications for cancer image analysis.

Affine registration of thermal images of plantar feet using convolutional neural networks

In a near of wide spread technological change that has givena positive impact to the society andhelpedinbuildingauser-friendly environment, object detection framework, an importantpart of Computer Vision (CV) plays a vital role. Starting fromasimpleautomaticattendancesystemforstudentsusingfacedetection, recognizing the presence of tumors in medical images,helping with automatic surveillance of cctv cameras to identifypeoplewhobreakstrafficrulescausingroadaccidentstobeingthecentral mechanism behind self-driving cars, object detection haswide range of applications and assist building an easy to cope withsmart environments. This in turn urges the need to evaluate theperformanceofthetechniquesbehindtheseframeworks.Thecentralideabehindthemodern-dayobjectdetectionandclassification is Convolutional Neural Network (CNN) which triesto mimic the occipital lobe, the visual cortex of the human brain.CNNhaswiderangeofvariationsandhascomethroughalongwaystarting from basic CV techniques like Scale Invariant FeatureTransform(SIFT),HistogramsofOrientedGradientsn(HOG)tillRegionbasedCNN’s(R-CNN).Theperformanceofeachandeverymethodthathas led throughthe evolutionofobjectdetectionmethods, its advantages and the disadvantages which has pavedway for the innovation of next technique has been discussed andrepresentedindetail.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems serve as a crucial defense mechanism against automated attacks by distinguishing between human users and bots. However, advancements in deep learning have posed significant challenges to the security of conventional CAPTCHA systems. In this research paper, we present a comparative analysis of two prominent object detection algorithms, Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks (RCNN), for breaking CAPTCHAs. The study evaluates the performance of CNN and RCNN algorithms in accurately identifying and deciphering characters within CAPTCHA images. Utilizing a diverse dataset of CAPTCHA samples, experiments are conducted to assess the effectiveness of both algorithms in handling variations in CAPTCHA styles, languages, and complexities. Through extensive experimentation and evaluation, we analyze the strengths and limitations of CNN and RCNN in the context of CAPTCHA breaking. Key metrics such as accuracy, precision, recall, and computational efficiency are compared to provide insights into the relative performance of each algorithm. The findings of this research contribute to the understanding of object detection techniques for CAPTCHA breaking and provide valuable insights for enhancing the security of CAPTCHA systems against emerging threats posed by deep learning-based attacks.