Detection of curved lines with B-COSFIRE filters: A case study on crack delineation

The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure=0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201

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

Automatic Assessment of Seed Germination Percentage

This research was designed to investigate an automatic seed germination rate for the top of paper germination method. Chili and guinea were adopted to be used in the experiment with a 4-time repetition and 2 sets of the germination group (4-separated plates with 50 seeds per plate, 2 sets per seed type, totally 400 seeds of chili and 400 seeds of quinea). Two detection methods were proposed binary thresholding and maximum likelihood; based on color analysis. An uncontrolled environment image taking was the way to collect image data. The results were compared to a hand-labeling groundtruth. Both methods achieved accuracy rate higher than 93% which was promising to implement this system. The binary thresholding was a lightweight method suitable for a very limited resource software environment system. The maximum likelihood was more complex. The method had more potential than the binary thresholding, it was flexible to the light condition, returned few false alarms per image (less than 3 false alarms per image). Maximum likelihood could be adopted to implement in a proper environment which still could be in a mobile device

Convolutional Neural Networks Exploiting Attributes of Biological Neurons

In this era of artificial intelligence, deep neural networks like Convolutional Neural Networks (CNNs) have emerged as front-runners, often surpassing human capabilities. These deep networks are often perceived as the panacea for all challenges. Unfortunately, a common downside of these networks is their ''black-box'' character, which does not necessarily mirror the operation of biological neural systems. Some even have millions/billions of learnable (tunable) parameters, and their training demands extensive data and time. Here, we integrate the principles of biological neurons in certain layer(s) of CNNs. Specifically, we explore the use of neuro-science-inspired computational models of the Lateral Geniculate Nucleus (LGN) and simple cells of the primary visual cortex. By leveraging such models, we aim to extract image features to use as input to CNNs, hoping to enhance training efficiency and achieve better accuracy. We aspire to enable shallow networks with a Push-Pull Combination of Receptive Fields (PP-CORF) model of simple cells as the foundation layer of CNNs to enhance their learning process and performance. To achieve this, we propose a two-tower CNN, one shallow tower and the other as ResNet 18. Rather than extracting the features blindly, it seeks to mimic how the brain perceives and extracts features. The proposed system exhibits a noticeable improvement in the performance (on an average of $5\%-10\%$) on CIFAR-10, CIFAR-100, and ImageNet-100 datasets compared to ResNet-18. We also check the efficiency of only the Push-Pull tower of the network.Comment: 20 pages, 6 figure

To Perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning Pipeline

Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant road signs. In this paper, we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs. The road signs classifier based on an artificial intelligence technique. In particular, a deep learning model is used, Convolutional Neural Networks (CNN). CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection. CNN has successfully used to solve computer vision problems because of its methodology in processing images that are similar to the human brain decision making. The evaluation of the proposed pipeline was trained and tested using two different datasets. The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8% and a testing accuracy of 99.6%. The proposed method can be easily implemented for real time application

A study on Image Caption using Double Embedding Technique and Bi-RNN

본 논문에서는 문장 표현력을 향상시키고 이미지 특징 벡터의 소멸을 방지할 수 있는 이중 Embedding 기법과 문맥에 맞는 문장 순서를 생성하는 Bidirectional Recurrent Neural Network(Bi-RNN)을 적용한 디테일한 이미지 캡션 모델을 제안한다. 이중 Embedding 기법에서, Word Embedding 과정인 EmbeddingⅠ은 캡션의 표현력을 향상시키기 위해 데이터세트의 캡션 단어를 One-hot encoding 방식을 통해 벡터화하고 EmbeddingⅡ는 캡션 생성 과정에서 발생하는 이미지 특징의 소멸을 방지하기 위해 이미지 특징 벡터와 단어 벡터를 융합함으로써 문장 구성 요소의 누락을 방지한다. 또한 디코더 영역은 어휘 및 이미지 특징을 양방향으로 획득하는 Bi-RNN으로 구성하여 문맥에 맞는 문장의 순서를 학습한다. 마지막으로 인코더와 디코더를 통하여 획득된 전체 이미지, 문장 표현, 문장 순서 특징들을 하나의 벡터공간인 Multimodal 레이어에 융합함으로써 문장의 순서와 표현력을 모두 고려한 디테일한 캡션을 생성한다. 제안하는 모델은 Flickr 8K 및 Flickr 30K, MSCOCO와 같은 이미지 캡션 데이터세트를 이용하여 학습 및 평가를 진행하였으며 객관적인 BLEU와 METEOR 점수를 통해 모델 성능의 우수성을 입증하였다. 그 결과, 제안한 모델은 3개의 다른 캡션 모델들에 비해 BLEU 점수는 최대 20.2점, METEOR 점수는 최대 3.65점이 향상되었다.|This thesis proposes a detailed image caption model that applies the double embedding technique to improve sentence expressiveness and to prevent vanishing of image feature vectors. It uses the bidirectional recurrent neural network (Bi-RNN) to generate a sequence of sentences and fit their contexts. In the double-embedding technique, embedding Ⅰ is a word-embedding process used to vectorize dataset captions through one-hot encoding to improve the expressiveness of the captions. Embedding Ⅱ prevents missed sentence components by fusing image features and word vectors to prevent image features from vanishing during caption generation. The decoder area, composed of a Bi-RNN that acquires vocabulary and image features in both directions, learns the sequence of sentences that fits their contexts. Finally, through the encoder and decoder, the detailed image caption is generated by considering both sequence and sentence expressiveness by fusing the acquired image features, sentence presentation features, and sentence sequence features into a multimodal layer as a vector space. The proposed model was learned and evaluated using image caption datasets (e.g., Flickr 8K, Flickr 30K, and MSCOCO). The proven BLEU and METEOR scores demonstrate the superiority of the model. The proposed model achieved a BLEU score maximum of 20.2 points and a METEOR score maximum of 3.65 points, which is higher than the scores of other three caption models.목 차 목 차 ⅰ 그림 및 표 목차 ⅱ Abstract ⅳ 제 1 장 서 론 01 제 2 장 뉴럴 네트워크 및 평가지표 04 2.1 Convolutional Neural Network 04 2.2 Recurrent Neural Network 08 2.3 Long Short-Term Memory 10 2.4 Gated Recurrent Unit 13 2.5 Bidirectional Recurrent Neural Network 15 2.6 Bi-Lingual Evaluation Understudy 17 2.7 Metric for Evaluation of Translation with Explicit ORdering 20 제 3 장 제안한 이미지 캡션 모델 23 3.1 이중 Embedding 기법과 Bi-RNN을 이용한 캡션 구성 과정 25 3.2 Multimodal 레이어를 이용한 캡션 생성 과정 27 제 4 장 실험 및 결과 29 4.1 데이터세트 및 전처리 과정 29 4.2 실험 결과 분석 31 제 5 장 결 론 41 참 고 문 헌 42Maste

Color-blob-based COSFIRE filters for object recognition

Most object recognition methods rely on contour-defined features obtained by edge detection or region segmentation. They are not robust to diffuse region boundaries. Furthermore, such methods do not exploit region color information. We propose color-blob-based COSFIRE (Combination of Shifted Filter Responses) filters to be selective for combinations of diffuse circular regions (blobs) in specific mutual spatial arrangements. Such a filter combines the responses of a certain selection of Difference-of-GausSians filters, essentially blob detectors, of different scales, in certain channels of a color space, and at certain relative positions to each other. Its parameters are determined learned in an automatic configuration process that analyzes the properties of a given prototype object of interest. We use these filters to compute features that are effective for the recognition of the prototype objects. We form feature vectors that we use With an SVM classifier. We evaluate the proposed method on a traffic sign (GTSRB) and a butterfly data sets. For the GTSRB data set we achieve a recognition rate of 98.94%, which is slightly higher than human performance and for the butterfly data set we achieve 89.029 The proposed color-blob-based COSFIRE filters are very effective and outperform the contour-based COSFIRE filters. A COSFIRE filter is trainable, it can be configured with a single prototype pattern and it does not require domain knowledge. (C) 2016 Elsevier B.V. All rights reserved