A Computational Model of Stereoscopic Prey Capture in Praying Mantises”

We present a simple model which can account for the stereoscopic sensitivity of praying mantis predatory strikes. The model consists of a single “disparity sensor”: a binocular neuron sensitive to stereoscopic disparity and thus to distance from the animal. The model is based closely on the known behavioural and neurophysiological properties of mantis stereopsis. The monocular inputs to the neuron reflect temporal change and are insensitive to contrast sign, making the sensor insensitive to interocular correlation. The monocular receptive fields have a excitatory centre and inhibitory surround, making them tuned to size. The disparity sensor combines inputs from the two eyes linearly, applies a threshold and then an exponent output nonlinearity. The activity of the sensor represents the model mantis’s instantaneous probability of striking. We integrate this over the stimulus duration to obtain the expected number of strikes in response to moving targets with different stereoscopic distance, size and vertical disparity. We optimised the parameters of the model so as to bring its predictions into agreement with our empirical data on mean strike rate as a function of stimulus size and distance. The model proves capable of reproducing the relatively broad tuning to size and narrow tuning to stereoscopic distance seen in mantis striking behaviour. The model also displays realistic responses to vertical disparity. Most surprisingly, although the model has only a single centre-surround receptive field in each eye, it displays qualitatively the same interaction between size and distance as we observed in real mantids: the preferred size increases as prey distance increases beyond the preferred distance. We show that this occurs because of a stereoscopic “false match” between the leading edge of the stimulus in one eye and its trailing edge in the other; further work will be required to find whether such false matches occur in real mantises. This is the first image-computable model of insect stereopsis, and reproduces key features of both neurophysiology and striking behaviour.

Record linkage under suboptimal conditions for data-intensive evaluation of primary care in Rio de Janeiro, Brazil

Background Linking Brazilian databases demands the development of algorithms and processes to deal with various challenges including the large size of the databases, the low number and poor quality of personal identifiers available to be compared (national security number not mandatory), and some characteristics of Brazilian names that make the linkage process prone to errors. This study aims to describe and evaluate the quality of the processes used to create an individual-linked database for data-intensive research on the impacts on health indicators of the expansion of primary care in Rio de Janeiro City, Brazil. Methods We created an individual-level dataset linking social benefits recipients, primary health care, hospital admission and mortality data. The databases were pre-processed, and we adopted a multiple approach strategy combining deterministic and probabilistic record linkage techniques, and an extensive clerical review of the potential matches. Relying on manual review as the gold standard, we estimated the false match (false-positive) proportion of each approach (deterministic, probabilistic, clerical review) and the missed match proportion (false-negative) of the clerical review approach. To assess the sensitivity (recall) to identifying social benefits recipients’ deaths, we used their vital status registered on the primary care database as the gold standard. Results In all linkage processes, the deterministic approach identified most of the matches. However, the proportion of matches identified in each approach varied. The false match proportion was around 1% or less in almost all approaches. The missed match proportion in the clerical review approach of all linkage processes were under 3%. We estimated a recall of 93.6% (95% CI 92.8–94.3) for the linkage between social benefits recipients and mortality data. Conclusion The adoption of a linkage strategy combining pre-processing routines, deterministic, and probabilistic strategies, as well as an extensive clerical review approach minimized linkage errors in the context of suboptimal data quality.

PENGEMBANGAN SISTEM KLASIFIKASI UKURAN PAKAIAN MENGGUNAKAN METODE BODY MEASUREMENT DAN FUZZY LOGIC BERBASIS SENSOR KINECT

Di dalam kehidupan sehari-hari, penentuan klasifikasi ukuran pakaian biasanya dilakukan dengan mencoba-coba pakaian terlebih dahulu sehingga membutuhkan waktu yang lebih lama. Dalam makalah ini, kita ingin membangun sistem untuk mengidentifikasi ukuran tubuh manusia dengan kamera. Selanjutnya dilakukan klasifikasi ukuran pakaian secara otomatis berdasarkan ukuran tubuh tersebut. Pendekatan yang diajukan untuk memecahkan hal ini adalah dengan metode body measurement dan Fuzzy Logic dengan menggunakan Kinect sebagai sensor. Metode body measurement digunakan untuk mengukur ukuran anggota tubuh manusia berdasarkan citra yang berasal dari sensor Kinect. Fuzzy Logic digunakan untuk menentukan klasifikasi ukuran pakaian berdasarkan hasil pengukuran yang diperoleh. Sistem pengukuran dak klasifikasi ini dapat mempermudah penentuan klasifikasi ukuran pakaian yang pas. Hasil dari penelitian ini menunjukkan bahwa sistem klasifikasi yang dibangun dapat menentukan ukuran pakaian dengan False Match Rate (FMR) sekitar 2.69%.

Combining deterministic and probabilistic matching to reduce data linkage errors in hospital administrative data

ABSTRACTObjectivesData linkage algorithms are used to link together multiple episodes of care belonging to the same patient. For example, the HESID algorithm is used to generate Hospital Episode Statistics (HES) in England. HESID is a deterministic algorithm, requiring identifiers to agree or disagree at each step. Data linkage errors occur when episodes belonging to two patients are incorrectly linked (a false match) or when episodes belonging to the same patient are not linked (a missed match). This typically occurs because patient identifiers (e.g. NHS number, postcode) contain errors or have missing data. We previously showed that HESID has a low false match rate (0.2%) but a high missed match rate (4.1%) when applied to paediatric intensive care data. This biased the true readmission rate, particularly for some patient groups including ethnic minorities. The aim of our study was to evaluate whether an additional step involving probabilistic matching would lower the missed match rate in HES without increasing the false matched rate. ApproachWe simulated three datasets having the same characteristics as HES, for three age groups expected to have different levels of postcode stability (at age 0/1, 5/6 and 18/19). We compared the deterministic algorithm to a probabilistic algorithm, and then to a deterministic algorithm with an additional probabilistic step. In sensitivity analyses, we evaluated the algorithms under different data quality scenarios. ResultsResults show that deterministic followed by probabilistic matching is the best solution for reducing missed matches, particularly in scenarios where errors in patient identifiers are more common. ConclusionData linkage algorithms need to be evaluated against good quality reference standard data sets. For hospital data in England, the Personal Demographics Service (PDS) could be used to evaluate our approach, because it contains many of the same patient identifiers used in HES. Reducing data linkage error will improve monitoring of hospital activity in England.

Deteksi Copy Move Forgery Pada Citra Menggunakan Exact Match, DWT Haar dan Daubechies

AbstrakCopy-Move Forgery adalah satu tipe gangguan citra digital, di mana bagian dari citra dicopy dan dipastekan ke bagian lain dalam citra yang sama untuk menutupi fitur citra yang penting. Pada penelitian ini, dibangun sistem pendeteksi copy move forgery pada citra. Sistem ini dimaksudkan untuk membantu user mengetahui bahwa suatu citra masih asli atau sudah terdapat copy move dan dibagian mana terjadinya copy move tersebut. Sistem ini dibangun dengan menggunakan metode Exact Match, DWT Haar, DWT DB2 dan DWT DB4 dengan menggunakan blok 4 x 4, 8 x 8 dan 16 x 16. Masukan dari sistem ini berupa citra input dan juga ukuran blok . Keluaran dari sistem ini adalah daerah yang terdeteksi sebagai copy move atau tidak terdeteksi sama sekali beserta dengan daerah yang di duga sebagai false match.Hasil akhir dari sistim ini ditunjukkan dalam bentuk akurasi, area false match dan waktu ekseukusi. Akurasi metode Exact Match untuk blok 4 x 4, 8 x 8 dan 16 x 16 adalah lebih baik dibandingkan dengan DWT walaupun memiliki area false match yang lebih besar. Akurasi dari DWT Haar, DWT db2 dan DWT db4 tergantung dari wilayah atau daerah copy move dalam citra. Blok 4 x 4 mempunyai area false match yang lebih besar dari blok 8 x 8 dan 16 x 16. Waktu eksekusi tergantung dari besar blok, semakin besar blok semakin besar waktu eksekusi.Kata kunci—copy move forgery, Exact Match, DWT Haar, DWT DB2, DWT DB4. AbstractCopy-Move Forgery is a special type of image forgery, in which a part of a digital image is copied and pasted to another part in the same image in order to cover an important image feature. This research developed a system to detect copy move forgery in digital image. The system is intended to help the user determine whether an image is authentic or already contained a copy move object, and if the image already contains copy move object, the system can determine in which section the copy move object is located. Copy move forgery detection system discussed in this research, was developed by using Exact Match, DWT Haar, DWT db2 and DWT db4 using blocks of 4 x 4, 8 x 8 and 16 x 16. Users can use the system by using the digital image as input. The output of the system is the information about the area detected as a copy move forgery along with areas suspected of being false match.The final result is shown in the form of accuracy, the area of the false match and execution time. Based on the test results, the accuracy of Exact Match method for blocks of 4 x 4, 8 x 8 and 16 x 16 is better than the DWT, although exact match have an bigger false match area. Accuracy of DWT Haar, DWT db2 and DWT DB4 depending on the copy move area on the image. Block 4 x 4 has a false match area larger than the block 8 x 8 and 16 x 16. The execution time depends on the size of the block, the larger the block, the longer the time of execution. Keywords— Copy move forgery, Exact Match, DWT Haar, DWT db2, DWT db4

Fingerprint Matching and Non-Matching Analysis for Different Tolerance Rotation Degrees in Commercial Matching Algorithms

Fingerprint verification is the most important step in the fingerprint-based biometric systems. The matching score is linked to the chance of identifying a person. Nowadays, two fingerprint matching methods are the most popular: the correlation-based method and the minutiae-based method. In this work, three biometric systems were evaluated: Neurotechnology Verifinger 6.0 Extended, Innovatrics IDKit SDK and Griaule Fingerprint SDK 2007. The evaluation was performed according to the experiments of the Fingerprint Verification Competition (FVC). The influence of the fingerprint rotation degrees on false match rate (FMR) and false non-match rate (FNMR) was evaluated. The results showed that the FMR values increase as rotation degrees increase too, meanwhile, the FNMR values decrease. Experimental results demonstrate that Verifinger SDK shows good performance on false non-match testing, with an FNMR mean of 7%, followed by IDKit SDK (6.71% ~ 13.66%) and Fingerprint SDK (50%). However, Fingerprint SDK demonstrates a better performance on false match testing, with an FMR mean of ~0%, followed by Verifinger SDK (7.62% - 9%) and IDKit SDK (above 28%). As result of the experiments, Verifinger SDK had, in general, the best performance. Subsequently, we calculated the regression functions to predict the behavior of FNMR and FMR for different threshold values with different rotation degrees.