U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.

MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the agesensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network.

Fine-Grained Age Estimation in the wild with Attention LSTM Networks

The field of biometrics research encompasses the need to associate an identity to an individual based on the persons physiological or behaviour traits. While the use of intrusive techniques such as retina scans and finger print identification has resulted in highly accurate systems, the scalability of such systems in real-world applications such as surveillance and border security has been limited. As a branch of biometrics research, the origin of soft biometrics could be traced back to need for non-intrusive solutions for extracting physiological traits of a person. Following high number of research outcomes reported in the literature on soft biometrics, this paper aims to consolidate the scope of soft biometrics research across four thematic schemes (i) a detailed review of soft biometrics research data sets, their annotation strategies and building a largest novel collection of soft traits; (ii) the assessment of metrics that affect the performance of soft biometrics system; (iii) a comparative analysis on feature and modality level fusion reported in the literature for enhancing the system performance; and (iv) a performance analysis of hybrid soft biometrics recognition system using multi-scale criterion. The paper also presents a detailed analysis on the global traits associated to person identity such as gender, age and ethnicity. The contribution of the paper is to provide a comprehensive review of scientific literature, identify open challenges and offer insights on new research directions in the filed.

/pdf/soft-biometrics-a-survey-3482js8ui5.pdf

Soft biometrics: a survey

Among the existing lossy compression methods, the transform coding is one of the most effective strategies. The Discrete Wavelet Transform (DWT) can be efficiently used in image coding applications because of its advantages as compared to the other transforms. A typical wavelet image compression system is composed of three connected components namely transformation, quantization and coding. In this paper, we review the recent improvements of each component. We present a detailed study of the recent implementation of the DWT as well as of its improvements. In addition, we describe the main principles of the wavelet-based compression schemes such as EZW, SPIHT, SPECK and EBCOT. We review the advantages and shortcomings of each of these algorithms. Also, we provide a survey of the recent improvements of the different coding schemes. Moreover, a comparative analysis of the recent enhancement and compression techniques is carried out in terms of visual quality and encoding time. We conclude by some guidelines which concern the design of an efficient codec for wavelet image compression using spline transform and improved coding scheme.

A comparative study of recent improvements in wavelet-based image coding schemes

Two-stages based facial demographic attributes combination for age estimation

In this paper, we propose a modification of the Shapiro's Embedded Zerotree Wavelet (EZW) algorithm. Our approach, called Modified EZW (MEZW), distributes entropy differently than Shapiro's by using six instead of four symbols used in EZW and also optimizes the coding by a binary grouping of elements before coding. This approach can produce results that are a significant improvement on the PSNR and compression ratio obtained by Shapiro, without affecting the computing time. These results are also comparable with those obtained using the SPIHT and SPECK algorithms.

A Modified Embedded Zerotree Wavelet (MEZW) Algorithm for Image Compression

In this paper, for skin lesion segmentation, we propose an encoder-decoder structure based on U-Net, combining dilated convolution and pyramid pooling module (PPM). The dilated convolution computes the feature maps with a high spatial resolution instead to down-sampling feature maps, and the aim of pyramid pooling module is to obtain more contextual information (multi-scale context information with multi-scale pooling). on the official test set of ISBI 2016, and in terms of three evaluation metrics, Our proposed model is tested and achieved better performance over U-Net and another published method with (JC=82.7, DC=89.6, SE =92.0).

A Modified U-Net for Skin Lesion Segmentation

An Improved and Robust Encoder–Decoder for Skin Lesion Segmentation

In this paper, a new approach of images coding by Shapiro algorithm (Embedded Zerotree Wavelet algorithm or EZW) is proposed. This approach, the modified EZW (MEZW), distributes entropy differently than Shapiro's and also optimizes the coding. This can produce results that are a significant improvement on the PSNR and compression ratio obtained by Shapiro, without affecting the computing time. These results are also comparable with those obtained using the SPIHT and SPECK algorithms. The EZW, Spiht or Speck algorithms are based on the Wavelet transform. The principle of wavelet transform is to decompose hierarchically the input image into a series of successively lower resolution reference images and detail images which contain the information needed to be reconstructed back to the next higher resolution level. The sub-band images resulting from wavelet transform are not of equal significance. Some sub-bands contain more information than others (example the baseband sub band).

A. Zitouni

Papers

Two-stages based facial demographic attributes combination for age estimation

A Modified Embedded Zerotree Wavelet (MEZW) Algorithm for Image Compression

A Modified U-Net for Skin Lesion Segmentation

An Improved and Robust Encoder–Decoder for Skin Lesion Segmentation

Color Image Coding by Modified Embedded Zerotree Wavelet (EZW) Algorithm