Place and Object Recognition by CNN-Based COSFIRE Filters

COSFIRE filters are an effective means for detecting and localizing visual patterns. In contrast to a convolutional neural network (CNN), such a filter can be configured by presenting a single training example and it can be applied on images of any size. The main limitation of COSFIRE filters so far was the use of only Gabor and DoGs contributing filters for the configuration of a COSFIRE filter. In this paper, we propose to use a much broader class of contributing filters, namely filters defined by intermediate CNN representations. We apply our proposed method on the MNIST data set, on the butterfly data set, and on a garden data set for place recognition, obtaining accuracies of 99.49%, 96.57%, and 89.84%, respectively. Our method outperforms a CNN-baseline method in which the full CNN representation at a certain layer is used as input to an SVM classifier. It also outperforms traditional non-CNN methods for the studied applications. In the case of place recognition, our method outperforms NetVLAD when only one reference image is used per scene and the two methods perform similarly when many reference images are used

Brain-Inspired Algorithms for Processing of Visual Data

The study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing. In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.</p

Deteksi Jumlah Percabangan pada Trabecular Bone Menggunakan COSFIRE Filter untuk Identifikasi Osteoporosis

Tulang rahang adalah salah satu tulang yang terkena pengaruh penurunan kepadatan mineral tulang yang diakibatkan oleh osteoporosis. Karena itu, citra radiograf panoramik gigi dapat digunakan untuk mengidentifikasi osteoporosis. Beberapa penelitian sebelumnya menunjukkan bahwa jumlah percabangan pada struktur tulang berbeda antara pasien normal dan pasien dengan kepadatan mineral tulang yang rendah. Namun, kontras yang rendah dan terdapatnya noise pada citra radiograf panoramik membuat ekstraksi struktur tulang menjadi sulit. Untuk itu, dibutuhkan sebuah metode untuk memperkuat struktur pada tulang tersebut.Pada penelitian ini, diusulkan sebuah metode untuk mendeteksi percabangan pada trabecular bone dengan enhancement pada struktur tulang menggunakan metode line operator. Dari struktur tersebut, lokasi percabangan dideteksi menggunakan metode COSFIRE. Kemudian, jumlah percabangan digunakan untuk membedakan antara radiograf pasien normal dan radiograf pasien osteoporosis.Pengujian klasifikasi dilakukan pada 98 citra yang terdiri atas 41 citra pasien osteoporosis dan 57 pasien normal. Hasilnya adalah sensitivity, specificity, dan akurasi masing-masing sebesar 0,90244, 0,23214, dan 0,51546. Hasil tersebut menunjukkan bahwa metode yang diusulkan menghasilkan performa yang lebih baik daripada metode sebelumnya.Tulang rahang adalah salah satu tulang yang terkena pengaruh penurunan kepadatan mineral tulang yang diakibatkan oleh osteoporosis. Karena itu, citra radiograf panoramik gigi dapat digunakan untuk mengidentifikasi osteoporosis. Beberapa penelitian sebelumnya menunjukkan bahwa jumlah percabangan pada struktur tulang berbeda antara pasien normal dan pasien dengan kepadatan mineral tulang yang rendah. Namun, kontras yang rendah dan terdapatnya noise pada citra radiograf panoramik membuat ekstraksi struktur tulang menjadi sulit. Untuk itu, dibutuhkan sebuah metode untuk memperkuat struktur pada tulang tersebut.Pada penelitian ini, diusulkan sebuah metode untuk mendeteksi percabangan pada trabecular bone dengan enhancement pada struktur tulang menggunakan metode line operator. Dari struktur tersebut, lokasi percabangan dideteksi menggunakan metode COSFIRE. Kemudian, jumlah percabangan digunakan untuk membedakan antara radiograf pasien normal dan radiograf pasien osteoporosis.Pengujian klasifikasi dilakukan pada 98 citra yang terdiri atas 41 citra pasien osteoporosis dan 57 pasien normal. Hasilnya adalah sensitivity, specificity, dan akurasi masing-masing sebesar 0,90244, 0,23214, dan 0,51546. Hasil tersebut menunjukkan bahwa metode yang diusulkan menghasilkan performa yang lebih baik daripada metode sebelumnya

Learning sound representations using trainable COPE feature extractors

Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR). In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined using a single prototype sound pattern in an automatic configuration process, which is a type of representation learning. We construct a set of COPE feature extractors, configured on a number of training patterns. Then we take their responses to build feature vectors that we use in combination with a classifier to detect and classify patterns of interest in audio signals. We carried out experiments on four public data sets: MIVIA audio events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund) demonstrate the effectiveness of the proposed method and are higher than the ones obtained by other existing approaches. The COPE feature extractors have high robustness to variations of SNR. Real-time performance is achieved even when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio

A survey of handwritten character recognition with MNIST and EMNIST

This article belongs to the Special Issue Computer Vision and Pattern Recognition in the Era of Deep Learning.This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have explored the performance of convolutional neural networks (CNNs) and other deep learning techniques over this dataset. To the best of our knowledge, this paper is the first exhaustive and updated review of this dataset; there are some online rankings, but they are outdated, and most published papers survey only closely related works, omitting most of the literature. This paper makes a distinction between those works using some kind of data augmentation and works using the original dataset out-of-the-box. Also, works using CNNs are reported separately; as they are becoming the state-of-the-art approach for solving this problem. Nowadays, a significant amount of works have attained a test error rate smaller than 1% on this dataset; which is becoming non-challenging. By mid-2017, a new dataset was introduced: EMNIST, which involves both digits and letters, with a larger amount of data acquired from a database different than MNIST's. In this paper, EMNIST is explained and some results are surveyed

Brain-Inspired Computing

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures

A robust contour detection operator with combined push-pull inhibition and surround suppression

Contour detection is a salient operation in many computer vision applications as it extracts features that are important for distinguishing objects in scenes. It is believed to be a primary role of simple cells in visual cortex of the mammalian brain. Many of such cells receive push-pull inhibition or surround suppression. We propose a computational model that exhibits a combination of these two phenomena. It is based on two existing models, which have been proven to be very effective for contour detection. In particular, we introduce a brain-inspired contour operator that combines push-pull and surround inhibition. It turns out that this combination results in a more effective contour detector, which suppresses texture while keeping the strongest responses to lines and edges, when compared to existing models. The proposed model consists of a Combination of Receptive Field (or CORF) model with push-pull inhibition, extended with surround suppression. We demonstrate the effectiveness of the proposed approach on the RuG and Berkeley benchmark data sets of 40 and 500 images, respectively. The proposed push-pull CORF operator with surround suppression outperforms the one without suppression with high statistical significance