Manual Ten Lectures on Statistical and Structural Pattern Recognition (Computational Imaging and Vision)

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Tuning of a text recognition algorithm Control Systems and Computers , 2 , Kiev, pp. Pavlyuk O. Effective parsing and recognition of structured images Control Systems and Computers , 5 , Kiev, pp. Parameters tuning in structural recognition problems Automatics, Proceedings of th international conference on automatic control , p. Parsing and recognition of printed music scores Automatics, Proceedings of th international conference on automatic control , p.

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Texture segmentation of images on the basis of Markov random fields Tech. Parsing and recognition of printed notes Control Systems and Computers , 4 , Kiev, pp. Computer technology for printed notes recognition Proc. Kiiko, V. Matsello Fast transformation of images in compressed form applied to drawings processing Proc.

Kijko, H. Masuch, G. Schade, G. Stanke, V. Schlesinger, V. Comparative analysis of stereovision algorithms in the framework of Bayes statistical decision theory Proceedings of the fifth all-ukrainian international conference UkrOBRAZ , Kiev, nov. Schlesinger Maximum likelihood estimation of the certain model of conditional independence Journal of Automation and Infomation Sciences, Vol.

Kijko, V. Matsello, H. November Aksak, Ch. Faist, V. Kijko, R. Knoffel, V. Jganovsky, M. Schlesinger, D. Schlesinger, G. Science Computer Analysis of Images and Patterns. Kiiko, M. Perner, P. Wang, A. Rosenfeld Eds. Advances in Structural and Syntactic Pattern Recognition. Our results suggest that the best low-level image processing for computer vision is different from existing algorithms designed to produce visually pleasing images.

The principles used to design the proposed architecture easily extend to other high-level computer vision tasks and image formation models, providing a general framework for integrating low-level and high-level image processing. The performance of deep networks trained for high-level computer vision tasks such as classification degrades under noise, blur, and other imperfections present in raw sensor data.

Left An image of jelly beans corrupted by noise characteristic of low-light conditions is misclassified as a library by the Inception-v4 classification network. Cleaning up raw data using conventional low-level image processing does not necessarily improve performance. Center The image denoised with BM3D is still misclassified, now as a vending machine. We propose an end-to-end differentiable architecture for joint denoising, deblurring, and classification that makes classification robust to realistic noise and blur.

The proposed architecture learns a denoising pipeline optimized for classification that enhances fine detail at the expense of more noise and artifacts. The proposed architecture has a principled and modular design inspired by formal optimization methods that generalizes to other combinations of image formation models and high-level computer vision tasks. Recent progress in deep learning has made it possible for computers to perform high-level tasks on images, such as classification, segmentation, and scene understanding.

High-level computer vision is useful for many real-world applications, including autonomous driving, robotics, and surveillance. Applying deep networks trained for high-level computer vision tasks to the outputs of real-world imaging systems can be difficult, however, because raw sensor data is often corrupted by noise, blur, and other imperfections.

What is the correct way to apply high-level networks to raw sensor data? Do effects such as noise and blur degrade network performance? If so, can the lost performance be regained by cleaning up the raw data with traditional image processing algorithms or by retraining the high-level network on raw data? Or is an entirely new approach to combining low-level and high-level image processing necessary to make deep networks robust? We examine these questions in the context of image classification under realistic camera noise and blur.

We show that realistic noise and blur can substantially reduce the performance of a classification architecture, even after retraining on noisy and blurry images or preprocessing the images with standard denoising and deblurring algorithms. We introduce a new architecture for combined denoising, deblurring, and classification that improves classification performance in difficult scenarios. The proposed architecture is end-to-end differentiable and based on a principled and modular approach to combining low-level image processing with deep architectures.

The architecture could be modified to handle a different image formation model or high-level computer vision task. We obtain superior performance by training the low-level image processing pipeline together with the classification network. The images output by the low-level image processing pipeline optimized for classification are qualitatively different from the images output by conventional denoising and deblurring algorithms, scoring worse on traditional reconstruction metrics such as peak signal-to-noise ratio PSNR.

The proposed architecture for joint denoising, deblurring, and classification makes classification robust and effective in real-world applications.

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The principles used to design the proposed architecture can be applied to make other high-level computer vision tasks robust to noise and blur, as well as to handle raw sensor data with more complex image formation models, such as RGB-D cameras and general sensor fusion. More broadly, the idea of combining low-level and high-level image processing within a jointly trained architecture opens up new possibilities for all of computational imaging.

We introduce a dataset of realistic noise and blur models calibrated from real-world cameras. We evaluate a classification architecture on images with realistic noise and blur and show substantial loss in performance. We propose a new end-to-end differentiable architecture that combines denoising and deblurring with classification, based on a principled and modular design inspired by formal optimization that can be applied to other image formation models and high-level tasks. We demonstrate that the proposed architecture, tuned on noisy and blurry images, substantially improves on the classification accuracy of the original network.

The joint architecture outperforms alternative approaches such as fine-tuning the classification architecture alone and preprocessing images with a conventional denoiser or deblurrer. We highlight substantial qualitative differences between the denoised and deblurred images output by the proposed architecture and those output by conventional denoisers and deblurrers, which suggest that the low-level image processing that is best for high-level computer vision tasks like classification is different than that which is best for producing visually pleasing images.

We evaluate the performance of the proposed architecture primarily in low-light conditions. We focus on classification in low-light both because it is important for real-world applications, such as autonomous driving and surveillance at night, and because out of the broad range of light levels for which we evaluated the classification network we found the largest drop in accuracy in low light both with and without blur.

If we can mitigate the effects of noise and blur under the most challenging conditions, then we can certainly do so for easier scenarios. A small body of work has explored the effects of noise and blur on deep networks trained for high-level computer vision tasks. Vasiljevic et al.

Chen et al. To the best of our knowledge we are the first to jointly train a denoiser or deblurrer combined with a high-level computer vision network in a pipeline architecture. The low-level image processing in the proposed joint architecture is based on unrolled optimization algorithms. If each iteration is differentiable in its output with respect to its parameters, the parameters of the unrolled algorithm can be optimized for a given loss through gradient based methods.

Ochs et al. Similarly, Chen et al. Both Schmidt and Roth and Chen et al. Conventional fully-connected or convolutional neural networks have also been successfully applied to low-level image processing tasks see, e. Heide et al. The measured image thus follows the simple but physically accurate Poisson-Gaussian noise model with clipping described by Foi et al.

For simplicity we did not include subsampling of color channels, as in a Bayer pattern, in the image formation model. Subsampling amplifies the effects of noise and blur, so whatever negative impact noise and blur have on classification accuracy would only be greater if subsampling was taken into account.

Computational Imaging and Vision

Nonetheless, we intend to expand the proposed joint denoising, deblurring, and classification architecture to include demosaicking in future work. Specifically, the PSFs k are estimated using a Bernoulli noise chart with checkerboard features, following Mosleh et al. The lens PSF varies spatially in the camera space, so we divided the field-of-view of the camera into non-overlapping blocks and carried out the PSF estimation for each individual block. The noise under our calibrated image formation model can be quite high, especially for low light levels. The noisy image in Fig.

This image was acquired for ISO and a 30 ms exposure time.

Research Project

The only image processing performed on this image was demosaicking. The severe levels of noise present in the image demonstrate that low and medium light conditions represent a major challenge for imaging and computer vision systems. Note that particularly inexpensive low-end sensors will exhibit drastically worse performance compared to higher end smartphone camera modules. An in-depth description of our calibration procedure is provided in the supplement. Upon acceptance, we will publically release our dataset of camera PSFs and noise curves.

We evaluated classification performance under the image formation model from Sec. We used PSFs from the center, offaxis, and periphery regions of the camera space. The three PSFs are highlighted in Fig. We used noise parameters for a variety of lux levels, ranging from moonlight to standard indoor lighting, derived from the ISO noise curves in Fig. The drop in performance for low light levels and for the periphery blur is dramatic. The results in Table. We fine-tune the network on training data passed through the image formation model.

We denoise and deblur images using standard algorithms before feeding them into the network. We train a novel architecture that combines denoising, deblurring, and classification, which we describe in Sec. We evaluate all three approaches in Sec. In this section, we describe the proposed architecture for joint denoising, deblurring, and classification, illustrated in Fig.

The architecture combines low-level and high-level image processing units in a pipeline that takes raw sensor data as input and outputs image labels. Our primary contribution is to make the architecture end-to-end differentiable through a principled approach based on formal optimization, allowing us to jointly train low-level and high-level image processing using efficient algorithms such as stochastic gradient descent SGD.

Ten Lectures on Statistical and Structural Pattern Recognition / Edition 1

Existing pipeline approaches, such as processing the raw sensor data with a camera ISP before applying a classification network, are not differentiable in the free parameters of the low-level image processing unit with respect to the pipeline output. We modify the shrinkage fields model using ideas from convolutional neural networks CNNs in order to increase the model capacity and make it better suited for training with SGD.

Any differentiable classification network can be used in the proposed pipeline architecture. The proposed architecture can be adapted to other high-level computer vision tasks such as segmentation, object detection, tracking, and scene understanding by replacing the classification network with a network for the given task. The outline of the section is as follows. In Sec. The proposed low-level image processing unit and the shrinkage fields model are inspired by the extensive literature on solving inverse problems in imaging via maximum-a-posteriori MAP estimation under a Bayesian model.


The MAP estimate of x is given by. Iterative methods are usually terminated based on a stopping condition that ensures theoretical convergence properties. An alternative approach is to execute a pre-determined number of iterations N , also known as unrolled optimization. One can interpret varying parameters as adaptive step sizes or as applying a single iteration of N different algorithms. We can thereby optimize the algorithm for a reconstruction metric such as PSNR or even the loss of a high-level network that operates on x N such as Inception-v4.

The choice of data term f y , A x is based on the physical characteristics of the sensor, which determine the image formation and noise model.

Ten Lectures on Statistical and Structural Pattern Recognition | M.I. Schlesinger | Springer

Classical priors are based on sparsity in a particular dual basis, i. Hand-crafted bases have few if any parameters.

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  • We need a richer parameterization in order to learn C. The most flexible parameterization for images assumes that C can be partitioned as. It follows that each C i is given by convolution with some filter c i. Learning C from data means learning the filters c 1 , … , c k. The norm g can also be learned from data. Many iterative methods do not evaluate g directly, but instead access g via its sub gradient or proximal operator. The proximal operator p r o x g is defined as.