IEEE International Conference On Image Processing Research Articles

Cardinal color fusion network for single image haze removal

Haze during bad weather degrades visibility of the scene drastically. Degradation of the scene visibility varies concerning the transmission map (TrMap) of the scene. Estimation of accurate TrMap is a key step to reconstruct the haze-free scene. Previous approaches, DehazeNet (Cai et al. in IEEE Trans Image Process 25(11):5187–5198, 2016), MSCNN (Ren et al., in: European conference on computer vision, Springer, New York, 2016), overcome the drawbacks of handcrafted features. However, they fail to recover the haze-free scene without color distortion. In this paper, cardinal color (red, green and blue) fusion network is proposed to estimate the robust TrMap. Proposed network recovers haze-free scene without color distortion. In the first stage, it generates multi-channel depth maps using a novel parallel convolution filter bank. The second stage comprises of multi-scale convolution filter bank which extracts the scale-invariant features. Further, spatial pooling followed by convolution filter estimates the robust scene transmission map. In the last stage, estimated TrMap is given to the optical model to recover the haze-free scene. Three benchmark datasets namely D-HAZY (Ancuti et al., in: 2016 IEEE international conference on image processing (ICIP), 2016), ImageNet (Deng et al., in: IEEE conference on computer vision and pattern recognition, 2009 (CVPR 2009), 2009) and a set of real-world hazy scenes are used to analyze the effectiveness of the proposed approach. Structural similarity index, mean square error and peak signal-to-noise ratio are used to evaluate the proposed network for single image haze removal. Further, the effect of haze on object detection is analyzed and robust cascaded pipeline, i.e., haze removal followed by Faster RCNN is proposed for object detection in the hazy environment. Performance analysis shows that the proposed network estimates robust TrMap and recovers haze-free scene without color distortion.

Deep learned Inter-Channel Colored Texture Pattern: a new chromatic-texture descriptor

Texture is a dominant tool for feature extraction. Incorporation of inter-channel chromatic information along with the texture feature will improve the accuracy of feature extraction. This paper provides deep learned Inter-Channel Colored Texture Pattern which gives the inter-channel chromatic texture information of an image. The information is extracted individually from the co-occurrent pixel values of various channels. It affords the unique channel-wise information and its relation with neighboring pixel information of opponent space. Deep learning with convolutional neural network is applied for learning the feature based on color and texture. The experiments for content-based image retrieval are carried out on three different databases which vary in nature: CIFAR-10 dataset (DB1) (Krizhevsky in Learning multiple layers of features from tiny images, University of Toronto, 2009), Corel database (DB2) (Corel 1000 and Corel 10000 image database, http://wang.ist.psu.edu/docs/related.shtml) and Facescrub dataset (DB3) (Ng and Winkler in 2014 IEEE international conference on image processing (ICIP), pp 343–347, 2014). Facescrub dataset is used for face recognition. The experimental analysis by applying this descriptor provides considerable improvement from the previous works for content-based image retrieval and face recognition.

HEp-2 Cell Classification via Combining Multiresolution Co-Occurrence Texture and Large Region Shape Information.

Indirect immunofluorescence imaging of human epithelial type 2 (HEp-2) cell image is an effective evidence to diagnose autoimmune diseases. Recently, computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has attracted great attention. However, the HEp-2 cell classification task is quite challenging due to large intraclass and small interclass variations. In this paper, we propose an effective approach for the automatic HEp-2 cell classification by combining multiresolution co-occurrence texture and large regional shape information. To be more specific, we propose to: 1) capture multiresolution co-occurrence texture information by a novel pairwise rotation-invariant co-occurrence of local Gabor binary pattern descriptor; 2) depict large regional shape information by using an improved Fisher vector model with RootSIFT features, which are sampled from large image patches in multiple scales; and 3) combine both features. We evaluate systematically the proposed approach on the IEEE International Conference on Pattern Recognition (ICPR) 2012, the IEEE International Conference on Image Processing (ICIP) 2013, and the ICPR 2014 contest datasets. The proposed method based on the combination of the introduced two features outperforms the winners of the ICPR 2012 contest using the same experimental protocol. Our method also greatly improves the winner of the ICIP 2013 contest under four different experimental setups. Using the leave-one-specimen-out evaluation strategy, our method achieves comparable performance with the winner of the ICPR 2014 contest that combined four features.

Does Parametric fMRI Analysis with SPM Yield Valid Results? – An Empirical Study of 1484 Rest Datasets

Does Parametric fMRI Analysis with SPM Yield Valid Results? – An Empirical Study of 1484 Rest Datasets

The land of pixels hosts ICIP 2009 [Conference Spotlight

Cairo, Egypt, will host the 2009 IEEE International Con ference on Image Processing (ICIP) 7-11 November 2009. This is the first time that ICIP will be held in the Middle East and only the second time that any major IEEE Signal Processing Society conference, ICASSP or ICIP, is taking place in this region (the first conference was ICASSP, which took place in Istanbul). ICIP 2009 is one of the main Society initiatives to reach out and have a signal processing global impact.

Need for a Revision of the Normal Limits of Resting Heart Rate

The current definition of sinus tachycardia is a heart rate >100 beats per minute (bpm).1 This limit was set arbitrarily when heart rate was not yet regarded as a risk factor for cardiovascular disease, probably with the main purpose of distinguishing between a disease state (fever, thyrotoxicosis, anemia, congestive heart failure, etc) and a normal condition. In recent years, interest has been aroused by the awareness that fast heart rate is a potent precursor of hypertension, atherosclerosis, and their sequelae.2 3 4 5 6 7 8 9 10 In addition, many leading epidemiological studies have shown that tachycardia is associated with an increased risk of death from cardiovascular and noncardiovascular causes. This relationship has been found in general populations,3 4 5 6 7 in elderly individuals,9 and in hypertensive cohorts.10 In all of these studies, the heart rate value above that in which a significant increase in risk was observed was below the 100 bpm threshold (Table 1⇓). Only in the study by Levy et al3 was tachycardia defined as a heart rate >99 bpm, but in that study the cutoff between normal and high heart rate was chosen arbitrarily, and the highest heart rate value measured during the examination was taken to define the subject’s heart rate. In all the other studies, the threshold level between normal and fast heart rate was between 79 and 90 bpm. View this table: Table 1. Heart Rate Values Above Which a Significant Increase in Risk Was Found: Data From 9 Epidemiological Studies The normalcy limits of a clinical variable can be established according to different criteria. For many parameters, such as most biochemical indexes, the 95% confidence interval is calculated to identify the upper normal limit of the variable. This statistical approach does not appear suitable for those clinical variables …