Workflow for reducing semantic segmentation annotation time

Abstract. Semantic segmentation is a challenging task within the field of pattern recognition from digital images. Current semantic segmentation methods that are based on neural networks show great promise in accurate pixel-level classification, but the methods seem to be limited at least to some extent by the availability of accurate training data. Semantic segmentation training data is typically curated by humans, but the task is rather slow and tedious even for humans. While humans are fast at checking whether a segmentation is accurate or not, creating segmentations is rather slow as the human visual system becomes limited by physical interfaces such as hand coordination for drawing segmentations by hand. This thesis evaluates a workflow that aims to reduce the need for drawing segmentations by hand to create an accurate set of training data. A publicly available dataset is used as the starting-point for the annotation process, and four different evaluation sets are used to evaluate the introduced annotation workflow in labour efficiency and annotation accuracy. Evaluation of the results indicates that the workflow can produce annotations that are comparable to manually corrected annotations in accuracy while requiring significantly less manual labour to produce annotations.Työnkulku semanttisen segmentoinnin annotointiajan vähentämiseen. Tiivistelmä. Semanttinen segmentointi on haastava osa-alue hahmontunnistusta digitaalisista kuvista. Tämänhetkiset semanttiset segmentaatiomenetelmät, jotka perustuvat neuroverkkoihin, osoittavat suurta potentiaalia tarkassa pikselitason luokittelussa, mutta ovat ainakin osittain tarkan koulutusdatan saatavuuden rajoittamia. Semanttisen segmentaation koulutusdata on tyypillisesti täysin ihmisten annotoimaa, mutta segmentaatioiden annotointi on hidasta ja pitkäveteistä. Vaikka ihmiset ovat nopeita tarkistamaan ovatko annotaatiot tarkkoja, niiden luonti on hidasta, koska ihmisen visuaalisen järjestelmän nopeuden ja tarkkuuden rajoittavaksi tekijäksi lisätään fyysinen rajapinta, kuten silmä-käsi-koordinaatio piirtäessä segmentaatioita käsin. Tämä opinnäytetyö arvioi kokonaisvaltaisen semanttisten segmentaatioiden annotointitavan, joka pyrkii vähentämään käsin piirtämisen tarvetta tarkan koulutusdatan luomiseksi. Julkisesti saatavilla olevaa datajoukkoa käytetään annotoinnin lähtökohtana, ja neljää erilaista evaluointijoukkoa käytetään esitetyn annotointitavan työtehokkuuden sekä annotaatiotarkkuuden arviointiin. Evaluaatiotulokset osoittavat, että esitetty tapa kykenee tuottamaan annotaatioita jotka ovat yhtä tarkkoja kuin käsin korjatut annotaatiot samalla merkittävästi vähentäen käsin tehtävän työn määrää

Deep Bilateral Learning for Real-Time Image Enhancement

Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation

Image segmentation is a fundamental and challenging problem in computer vision with applications spanning multiple areas, such as medical imaging, remote sensing, and autonomous vehicles. Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines. Although CNN-based models are adept at learning abstract features from raw image data, their performance is dependent on the availability and size of suitable training datasets. Additionally, these models are often unable to capture the details of object boundaries and generalize poorly to unseen classes. In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream computer vision. In particular, our contributions include (1) state-of-the-art 2D and 3D image segmentation networks for computer vision and medical image analysis, (2) an end-to-end trainable image segmentation framework that unifies CNNs and active contour models with learnable parameters for fast and robust object delineation, (3) a novel approach for disentangling edge and texture processing in segmentation networks, and (4) a novel few-shot learning model in both supervised settings and semi-supervised settings where synergies between latent and image spaces are leveraged to learn to segment images given limited training data.Comment: PhD dissertation, UCLA, 202

Clustering Optimized Portrait Matting Algorithm Based on Improved Sparrow Algorithm

As a result of the influence of individual appearance and lighting conditions, aberrant noise spots cause significant mis-segmentation for frontal portraits. This paper presents an accurate portrait segmentation approach based on a combination of wavelet proportional shrinkage and an upgraded sparrow search (SSA) clustering algorithm to solve the accuracy challenge of segmentation for frontal portraits. The brightness component of the human portrait in HSV space is first subjected to wavelet scaling denoising. The elite inverse learning approach and adaptive weighting factor are then implemented to optimize the initial center location of the K-Means algorithm to improve the initial distribution and accelerate the convergence speed of SSA population members. The pixel segmentation accuracy of the proposed method is approximately 70% and 15% higher than two comparable traditional methods, while the similarity of color image features is approximately 10% higher. Experiments show that the proposed method has achieved a high level of accuracy in capricious lighting conditions